Research topic

I want to find review or survey papers on multi-document question answering (MDQA) from 2020 onwards.

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Topic Match
Cit./Year
Year
Paper
Paper Relevance Summary
100.0%
20.8
2023
[1] Knowledge Graph Prompting for Multi-Document Question Answering Yu Wang, ..., and Tyler Derr AAAI Conference on Artificial Intelligence 2023 - 28 citations - Show abstract - Cite - PDF 100.0% topic match
Proposes Knowledge Graph Prompting for MDQA. Introduces graph-based context formulation using knowledge graphs for logical associations in multi-document contexts. Published in 2023, focuses on leveraging LLMs for MDQA, clearly relevant to advanced MDQA methodologies.
Proposes Knowledge Graph Prompting for MDQA. Introduces graph-based context formulation using knowledge graphs for logical associations in multi-document contexts. Published in 2023, focuses on leveraging LLMs for MDQA, clearly relevant to advanced MDQA methodologies.
100.0%
1.1
2024
[2] HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents QA Xinyue Chen, ..., and Xiaoyang Tan ArXiv 2024 - 1 citations - Show abstract - Cite - PDF 100.0% topic match
Presents an advanced MDQA framework and new benchmark. Describes HiQA, integrating cascading metadata and multi-route retrieval for state-of-the-art MDQA performance. Introduces the MasQA benchmark for evaluating MDQA; focuses on recent advancements (post-2020).
Presents an advanced MDQA framework and new benchmark. Describes HiQA, integrating cascading metadata and multi-route retrieval for state-of-the-art MDQA performance. Introduces the MasQA benchmark for evaluating MDQA; focuses on recent advancements (post-2020).
100.0%
10.0
2023
[3] Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering Avi Caciularu, ..., and Arman Cohan Annual Meeting of the Association for Computational Linguistics 2023 - 16 citations - Show abstract - Cite - PDF 100.0% topic match
Proposes a novel multi-document QA pre-training objective. Generates semantically-oriented questions and recovers sentences using cross-document information. Focuses on improving both short and long text generation for multi-document tasks, evaluated on MDQA and summarization.
Proposes a novel multi-document QA pre-training objective. Generates semantically-oriented questions and recovers sentences using cross-document information. Focuses on improving both short and long text generation for multi-document tasks, evaluated on MDQA and summarization.
100.0%
1.4
2024
[4] CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting Zukang Yang and Zixuan Zhu ArXiv 2024 - 1 citations - Show abstract - Cite - PDF 100.0% topic match
Proposes a reasoning-infused framework for multi-document question answering. Combines a knowledge graph with a large language model to improve reasoning and accuracy. Focuses on advanced reasoning and efficiency, relevant to reducing hallucination and cost in MDQA.
Proposes a reasoning-infused framework for multi-document question answering. Combines a knowledge graph with a large language model to improve reasoning and accuracy. Focuses on advanced reasoning and efficiency, relevant to reducing hallucination and cost in MDQA.
100.0%
0.8
2021
[5] Scaling Up Query-Focused Summarization to Meet Open-Domain Question Answering Weijia Zhang, ..., and E. Kanoulas ArXiv 2021 - 3 citations - Show abstract - Cite 100.0% topic match
Extends query-focused summarization to open-domain QA. Combines passage retrieval with text generation to summarize retrieved documents based on a query. Focuses on a task similar to MDQA, making it somewhat related. Published in 2021.
Extends query-focused summarization to open-domain QA. Combines passage retrieval with text generation to summarize retrieved documents based on a query. Focuses on a task similar to MDQA, making it somewhat related. Published in 2021.
100.0%
8.0
2020
[6] WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization Md Tahmid Rahman Laskar, ..., and J. Huang International Conference on Computational Linguistics 2020 - 33 citations - Show abstract - Cite - PDF 100.0% topic match
Proposes a weakly supervised learning approach for QF-MDS. Utilizes distant supervision and pre-trained models to generate weak reference summaries for training. Focuses on query-focused multi-document summarization with potential application in MDQA; published in 2020.
Proposes a weakly supervised learning approach for QF-MDS. Utilizes distant supervision and pre-trained models to generate weak reference summaries for training. Focuses on query-focused multi-document summarization with potential application in MDQA; published in 2020.
100.0%
6.9
2021
[7] Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices Hariom A. Pandya and Brijesh S. Bhatt ArXiv 2021 - 21 citations - Show abstract - Cite - PDF 100.0% topic match
Provides a broad review of the question answering field. Analyzes QA based on types of questions, answers, and evidence sources. It addresses multi-document sources but primarily focuses on general QA methods and challenges, with limited emphasis on MDQA.
Provides a broad review of the question answering field. Analyzes QA based on types of questions, answers, and evidence sources. It addresses multi-document sources but primarily focuses on general QA methods and challenges, with limited emphasis on MDQA.
99.8%
10.6
2021
[8] Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization Md Tahmid Rahman Laskar, ..., and J. Huang Computational Linguistics 2021 - 32 citations - Show abstract - Cite - PDF 99.8% topic match
Utilizes pre-trained transformers for query-focused summarization. Explores domain adaptation techniques to generate abstractive summaries for both single and multi-document scenarios. Somewhat related; discusses multi-document summarization but may not focus deeply on MDQA-specific challenges.
Utilizes pre-trained transformers for query-focused summarization. Explores domain adaptation techniques to generate abstractive summaries for both single and multi-document scenarios. Somewhat related; discusses multi-document summarization but may not focus deeply on MDQA-specific challenges.
90.4%
4.2
2020
[9] Answering Any-hop Open-domain Questions with Iterative Document Reranking Yuyu Zhang, ..., and Le Song Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2020 - 18 citations - Show abstract - Cite - PDF 90.4% topic match
Proposes a unified QA framework for any-hop open-domain questions. Iteratively retrieves, reranks, and filters documents, employing a graph-based reranking model. Partially relevant: focuses on multi-hop rather than multi-document QA and does not primarily review MDQA.
Proposes a unified QA framework for any-hop open-domain questions. Iteratively retrieves, reranks, and filters documents, employing a graph-based reranking model. Partially relevant: focuses on multi-hop rather than multi-document QA and does not primarily review MDQA.
85.8%
5.4
2020
[10] Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? Yixuan Tang, ..., and A. Tung Conference of the European Chapter of the Association for Computational Linguistics 2020 - 26 citations - Show abstract - Cite - PDF 85.8% topic match
First Bullet Point: Investigates single-hop sub-question handling in multi-hop QA. Second Bullet Point: Proposes sub-question evaluation on HotpotQA to assess multi-hop question answering systems' reasoning processes. Third Bullet Point: Relevant to multi-document QA for its focus on reasoning from multiple passages, but not a comprehensive review or survey.
First Bullet Point: Investigates single-hop sub-question handling in multi-hop QA. Second Bullet Point: Proposes sub-question evaluation on HotpotQA to assess multi-hop question answering systems' reasoning processes. Third Bullet Point: Relevant to multi-document QA for its focus on reasoning from multiple passages, but not a comprehensive review or survey.
54.3%
299.8
2023
[11] Retrieval-Augmented Generation for Large Language Models: A Survey Yunfan Gao, ..., and Haofen Wang ArXiv 2023 - 307 citations - Show abstract - Cite - PDF 54.3% topic match
Provides a comprehensive review of Retrieval-Augmented Generation (RAG) techniques for Large Language Models (LLMs). Discusses how RAG enhances LLMs by integrating external knowledge for more accurate, credible, and up-to-date information generation. While relevant to enhancing knowledge-intensive tasks, the focus is not explicitly on multi-document question answering (MDQA).
Provides a comprehensive review of Retrieval-Augmented Generation (RAG) techniques for Large Language Models (LLMs). Discusses how RAG enhances LLMs by integrating external knowledge for more accurate, credible, and up-to-date information generation. While relevant to enhancing knowledge-intensive tasks, the focus is not explicitly on multi-document question answering (MDQA).
48.5%
0.0
2023
[12] Graph Attention with Hierarchies for Multi-hop Question Answering Yunjie He, ..., and Pontus Stenetorp ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 48.5% topic match
Presents extensions to hierarchical graph networks for multi-hop QA. Explores improvements in graph attention and hierarchy usage on HotpotQA benchmark. Focuses on model enhancements rather than providing a comprehensive survey on MDQA. Not a review paper.
Presents extensions to hierarchical graph networks for multi-hop QA. Explores improvements in graph attention and hierarchy usage on HotpotQA benchmark. Focuses on model enhancements rather than providing a comprehensive survey on MDQA. Not a review paper.
33.4%
5.9
2023
[13] Complex QA and language models hybrid architectures, Survey Xavier Daull, ..., and Elisabeth Murisasco Journal Not Provided 2023 - 11 citations - Show abstract - Cite - PDF 33.4% topic match
Provides a review of hybrid architectures for complex question answering (CQA). Focuses on leveraging large language models (LLMs) for domain-specific, multi-step QA tasks. Mentions recent projects and evaluation techniques but doesn't explicitly address MDQA, only CQA and long-form QA.
Provides a review of hybrid architectures for complex question answering (CQA). Focuses on leveraging large language models (LLMs) for domain-specific, multi-step QA tasks. Mentions recent projects and evaluation techniques but doesn't explicitly address MDQA, only CQA and long-form QA.
21.0%
10.1
2020
[14] AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization Sayali Kulkarni, ..., and Eugene Ie ArXiv 2020 - 42 citations - Show abstract - Cite - PDF 21.0% topic match
Proposes a method for generating qMDS datasets. Introduces AQuaMuSe to create scalable query-based multi-document summarization datasets from QA datasets. Focuses on qMDS, potentially relevant to MDQA by contributing datasets for related summarization tasks.
Proposes a method for generating qMDS datasets. Introduces AQuaMuSe to create scalable query-based multi-document summarization datasets from QA datasets. Focuses on qMDS, potentially relevant to MDQA by contributing datasets for related summarization tasks.
19.3%
10.2
2021
[15] Data Augmentation for Abstractive Query-Focused Multi-Document Summarization Ramakanth Pasunuru, ..., and Jianfeng Gao AAAI Conference on Artificial Intelligence 2021 - 39 citations - Show abstract - Cite - PDF 19.3% topic match
15.7%
37.9
2021
[16] Hurdles to Progress in Long-form Question Answering Kalpesh Krishna, ..., and Mohit Iyyer North American Chapter of the Association for Computational Linguistics 2021 - 144 citations - Show abstract - Cite - PDF 15.7% topic match
Discusses challenges and evaluation in long-form question answering (LFQA). Highlights issues with dataset overlap, answer grounding, and evaluation metrics in LFQA. Focus is on LFQA more broadly, with limited direct emphasis on MDQA.
Discusses challenges and evaluation in long-form question answering (LFQA). Highlights issues with dataset overlap, answer grounding, and evaluation metrics in LFQA. Focus is on LFQA more broadly, with limited direct emphasis on MDQA.
14.6%
11.9
2022
[17] Multi-hop Question Answering Vaibhav Mavi, ..., and A. Jatowt Foundations and Trends® in Information Retrieval 2022 - 32 citations - Show abstract - Cite - PDF 14.6% topic match
Focuses on Multi-Hop Question Answering (MHQA): Analyzes tasks involving multiple steps of reasoning: Discusses datasets, models, and evaluation strategies specific to MHQA, not directly MDQA. Insight on combining multiple pieces of information: Relevant techniques can be useful but does not explicitly review or survey MDQA from 2020 onwards.
Focuses on Multi-Hop Question Answering (MHQA): Analyzes tasks involving multiple steps of reasoning: Discusses datasets, models, and evaluation strategies specific to MHQA, not directly MDQA. Insight on combining multiple pieces of information: Relevant techniques can be useful but does not explicitly review or survey MDQA from 2020 onwards.
14.2%
0.4
2022
[18] Ask to Understand: Question Generation for Multi-hop Question Answering Jiawei Li, ..., and Yizhe Yang ArXiv 2022 - 1 citations - Show abstract - Cite - PDF 14.2% topic match
Proposes a novel method for multi-hop question answering. Uses question generation (QG) to decompose complex questions into simple sub-questions for better interpretability and performance. Focuses on multi-hop QA rather than a comprehensive review of MDQA, making it somewhat related but not a direct review or survey of MDQA.
Proposes a novel method for multi-hop question answering. Uses question generation (QG) to decompose complex questions into simple sub-questions for better interpretability and performance. Focuses on multi-hop QA rather than a comprehensive review of MDQA, making it somewhat related but not a direct review or survey of MDQA.
12.9%
3.3
2024
[19] Desiderata For The Context Use Of Question Answering Systems Sagi Shaier, ..., and K. Wense ArXiv 2024 - 3 citations - Show abstract - Cite - PDF 12.9% topic match
Provides a survey of QA models' desiderata. Reviews existing and novel criteria across multiple QA systems and datasets. Focuses on overall QA models, not specifically on MDQA; relevance may be limited if MDQA isn't a primary focus.
Provides a survey of QA models' desiderata. Reviews existing and novel criteria across multiple QA systems and datasets. Focuses on overall QA models, not specifically on MDQA; relevance may be limited if MDQA isn't a primary focus.
12.7%
1.2
2023
[20] Review on Query-focused Multi-document Summarization (QMDS) with Comparative Analysis Prasenjeet Roy and Suman Kundu ACM Computing Surveys 2023 - 2 citations - Show abstract - Cite 12.7% topic match
What the paper does: Provides a review of QMDS methodologies. Details: Systematically reviews six major categories of query-focused multi-document summarization techniques and evaluates them. Relevance: Focuses on summarization rather than question answering; limited direct relevance to MDQA despite overlap in multi-document handling.
What the paper does: Provides a review of QMDS methodologies. Details: Systematically reviews six major categories of query-focused multi-document summarization techniques and evaluates them. Relevance: Focuses on summarization rather than question answering; limited direct relevance to MDQA despite overlap in multi-document handling.
12.4%
1.3
2023
[21] LMGQS: A Large-scale Dataset for Query-focused Summarization Ruochen Xu, ..., and Michael Zeng Conference on Empirical Methods in Natural Language Processing 2023 - 2 citations - Show abstract - Cite - PDF 12.4% topic match
11.8%
3.5
2021
[22] NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization S. Yadav, ..., and D. Gupta Workshop on Biomedical Natural Language Processing 2021 - 14 citations - Show abstract - Cite - PDF 11.8% topic match
Provides: Evaluation of transfer learning for multi-answer summarization. Details: Uses pre-trained transformer models for abstractive question summarization and extractive multi-answer summarization in consumer health. Relevance: Not a comprehensive review of MDQA; focuses on specific methodologies for a healthcare Q&A challenge.
Provides: Evaluation of transfer learning for multi-answer summarization. Details: Uses pre-trained transformer models for abstractive question summarization and extractive multi-answer summarization in consumer health. Relevance: Not a comprehensive review of MDQA; focuses on specific methodologies for a healthcare Q&A challenge.
11.7%
2.7
2023
[23] Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset Zafaryab Rasool, ..., and Alex Bahar-Fuchs ArXiv 2023 - 3 citations - Show abstract - Cite - PDF 11.7% topic match
First Bullet Point: Evaluates LLM performance on document-based QA using CogTale dataset. Second Bullet Point: Focuses on exact answer selection and numerical extraction in zero-shot settings with GPT-4 and GPT-3.5. Third Bullet Point: Does not explicitly cover multi-document question answering; primarily assesses performance on single document-based QA tasks.
First Bullet Point: Evaluates LLM performance on document-based QA using CogTale dataset. Second Bullet Point: Focuses on exact answer selection and numerical extraction in zero-shot settings with GPT-4 and GPT-3.5. Third Bullet Point: Does not explicitly cover multi-document question answering; primarily assesses performance on single document-based QA tasks.
11.5%
3.4
2024
[24] An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration Yihao Li, ..., and Gongshen Liu ArXiv 2024 - 3 citations - Show abstract - Cite - PDF 11.5% topic match
Proposes a KG-LLM collaborative reasoning scheme. Introduces a method using knowledge graphs to enhance LLMs' reasoning transparency and reliability. Targets general NLP challenges, not explicitly focused on MDQA or providing a review/survey.
Proposes a KG-LLM collaborative reasoning scheme. Introduces a method using knowledge graphs to enhance LLMs' reasoning transparency and reliability. Targets general NLP challenges, not explicitly focused on MDQA or providing a review/survey.
11.3%
3.3
2021
[25] Biomedical Question Answering: A Comprehensive Review Qiao Jin, ..., and Sheng Yu ArXiv 2021 - 13 citations - Show abstract - Cite 11.3% topic match
11.2%
0.0
2024
[26] DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering Zijian Hei, ..., and Yi Lin Journal Not Provided 2024 - 0 citations - Show abstract - Cite - PDF 11.2% topic match
11.0%
1.9
2021
[27] A Brief Survey of Question Answering Systems Michael Caballero International Journal of Artificial Intelligence & Applications 2021 - 6 citations - Show abstract - Cite 11.0% topic match
10.9%
1.0
2022
[28] A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation Minjun Zhu, ..., and Fei Xia Workshop on Document-grounded Dialogue and Conversational Question Answering 2022 - 3 citations - Show abstract - Cite - PDF 10.9% topic match
10.7%
1.2
2024
[29] GenDec: A robust generative Question-decomposition method for Multi-hop reasoning Jian Wu, ..., and Manabu Okumura ArXiv 2024 - 1 citations - Show abstract - Cite - PDF 10.7% topic match
10.5%
0.0
2023
[30] Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Models Yin Zhu, ..., and Gong Cheng ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 10.5% topic match
Evaluates retrieval-augmented methods for multi-hop question answering (MHQA). Discusses integrating large language models (LLMs) with iterative information retrieval techniques. Focuses on multi-hop QA but not specifically on multi-document QA; may be marginally relevant.
Evaluates retrieval-augmented methods for multi-hop question answering (MHQA). Discusses integrating large language models (LLMs) with iterative information retrieval techniques. Focuses on multi-hop QA but not specifically on multi-document QA; may be marginally relevant.
10.4%
3.8
2021
[31] Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections Wei-Jen Ko, ..., and Junyi Jessy Li Conference on Empirical Methods in Natural Language Processing 2021 - 12 citations - Show abstract - Cite - PDF 10.4% topic match
Provides: A framework for discourse comprehension via question answering. Details: Introduces DCQA corpus for linking sentences through open-ended questions in news documents. Relevance: Focuses on discourse comprehension; less emphasis on multi-document question answering (MDQA).
Provides: A framework for discourse comprehension via question answering. Details: Introduces DCQA corpus for linking sentences through open-ended questions in news documents. Relevance: Focuses on discourse comprehension; less emphasis on multi-document question answering (MDQA).
10.3%
4.8
2019
[32] Do Multi-hop Readers Dream of Reasoning Chains? Haoyu Wang, ..., and Tian Gao Conference on Empirical Methods in Natural Language Processing 2019 - 25 citations - Show abstract - Cite - PDF 10.3% topic match
10.1%
0.5
2021
[33] Intelligent Question Answering Module for Product Manuals Abinaya Govindan, ..., and Usa Neuron7.ai Journal Not Provided 2021 - 2 citations - Show abstract - Cite 10.1% topic match
10.0%
2.1
2020
[34] Query Focused Multi-Document Summarization with Distant Supervision Yumo Xu and Mirella Lapata ArXiv 2020 - 10 citations - Show abstract - Cite - PDF 10.0% topic match
9.9%
6.4
2022
[35] Document Summarization with Latent Queries Yumo Xu and Mirella Lapata Transactions of the Association for Computational Linguistics 2022 - 17 citations - Show abstract - Cite - PDF 9.9% topic match
9.6%
2.0
2021
[36] Extractive Multi-Document Summarization: A Review of Progress in the Last Decade Zakia Jalil, ..., and Muhammad Nasir IEEE Access 2021 - 8 citations - Show abstract - Cite 9.6% topic match
Provides a review of extractive multi-document summarization techniques. Surveys advancements and techniques in extractive multi-document summarization over the last decade, including benchmarks and evaluations. Relevant to MDQA but focuses solely on summarization, not specifically on question answering.
Provides a review of extractive multi-document summarization techniques. Surveys advancements and techniques in extractive multi-document summarization over the last decade, including benchmarks and evaluations. Relevant to MDQA but focuses solely on summarization, not specifically on question answering.
9.3%
1.0
2023
[37] Query Refinement Prompts for Closed-Book Long-Form QA Reinald Kim Amplayo, ..., and Shashi Narayan Annual Meeting of the Association for Computational Linguistics 2023 - 2 citations - Show abstract - Cite - PDF 9.3% topic match
Shows methods for improving long-form QA using query refinement prompts. Explores techniques in closed-book settings that involve multifaceted questions requiring information from multiple sources. Focuses on long-form question answering generally; does not explicitly review or survey MDQA techniques.
Shows methods for improving long-form QA using query refinement prompts. Explores techniques in closed-book settings that involve multifaceted questions requiring information from multiple sources. Focuses on long-form question answering generally; does not explicitly review or survey MDQA techniques.
9.2%
1.7
2023
[38] DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text Wenting Zhao, ..., and Semih Yavuz ArXiv 2023 - 2 citations - Show abstract - Cite - PDF 9.2% topic match
9.0%
0.0
2024
[39] LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization Masafumi Enomoto, ..., and M. Oyamada Journal Not Provided 2024 - 0 citations - Show abstract - Cite - PDF 9.0% topic match
8.8%
0.5
2008
[40] MultiSum: Query-Based Multi-Document Summarization M. Rosner and C. Camilleri International Conference on Computational Linguistics 2008 - 8 citations - Show abstract - Cite - PDF 8.8% topic match
8.5%
2.8
2022
[41] Query Refinement Prompts for Closed-Book Long-Form Question Answering Reinald Kim Amplayo, ..., and Shashi Narayan ArXiv 2022 - 6 citations - Show abstract - Cite - PDF 8.5% topic match
8.5%
2.7
2019
[42] Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks Mokanarangan Thayaparan, ..., and A. Freitas Conference on Empirical Methods in Natural Language Processing 2019 - 14 citations - Show abstract - Cite - PDF 8.5% topic match
8.1%
7.3
2020
[43] Generating Query Focused Summaries from Query-Free Resources Yumo Xu and Mirella Lapata Annual Meeting of the Association for Computational Linguistics 2020 - 29 citations - Show abstract - Cite - PDF 8.1% topic match
8.1%
1.7
2020
[44] Corpora Evaluation and System Bias detection in Multi Document Summarization Alvin Dey, ..., and Tanmoy Chakraborty ArXiv 2020 - 7 citations - Show abstract - Cite - PDF 8.1% topic match
7.9%
0.0
2023
[45] A Dataset and Multi-task Multi-view Approach for Question-Answering with the Dual Perspectives of Text and Knowledge MS Adithya, ..., and Bhaskarjyothi Das 2023 15th International Conference on Computer and Automation Engineering (ICCAE) 2023 - 0 citations - Show abstract - Cite 7.9% topic match
Shows integration of text and knowledge graph for question-answering. Combines structural information from KGs with semantic NL context. Focuses on a multi-view dataset and multi-task learning, not MDQA.
Shows integration of text and knowledge graph for question-answering. Combines structural information from KGs with semantic NL context. Focuses on a multi-view dataset and multi-task learning, not MDQA.
7.8%
0.0
2023
[46] Can Anaphora Resolution Improve Extractive Query-Focused Multi-Document Summarization? Salima Lamsiyah, ..., and Christoph Schommer IEEE Access 2023 - 0 citations - Show abstract - Cite 7.8% topic match
7.6%
51.5
2022
[47] GreaseLM: Graph REASoning Enhanced Language Models for Question Answering Xikun Zhang, ..., and J. Leskovec ArXiv 2022 - 151 citations - Show abstract - Cite - PDF 7.6% topic match
7.3%
4.0
2022
[48] Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval John Giorgi, ..., and Arman Cohan Conference on Empirical Methods in Natural Language Processing 2022 - 8 citations - Show abstract - Cite - PDF 7.3% topic match
7.3%
17.1
2020
[49] Coarse-to-Fine Query Focused Multi-Document Summarization Yumo Xu and Mirella Lapata Conference on Empirical Methods in Natural Language Processing 2020 - 71 citations - Show abstract - Cite - PDF 7.3% topic match
7.2%
4.4
2023
[50] SEMQA: Semi-Extractive Multi-Source Question Answering Tal Schuster, ..., and Donald Metzler ArXiv 2023 - 5 citations - Show abstract - Cite - PDF 7.2% topic match
6.8%
0.3
2021
[51] New Methods & Metrics for LFQA tasks Suchismit Mahapatra, ..., and Prasanna Kumar ArXiv 2021 - 1 citations - Show abstract - Cite - PDF 6.8% topic match
6.7%
0.5
2023
[52] MoQA: Benchmarking Multi-Type Open-Domain Question Answering Howard Yen, ..., and Danqi Chen Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering 2023 - 1 citations - Show abstract - Cite - PDF 6.7% topic match
6.6%
5.5
2012
[53] Automatic Multi Document Summarization Approaches Y. J. Kumar and N. Salim Journal of Computer Science 2012 - 71 citations - Show abstract - Cite 6.6% topic match
6.6%
1.0
2022
[54] Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization David Adams, ..., and Yllias Chali ArXiv 2022 - 2 citations - Show abstract - Cite - PDF 6.6% topic match
6.3%
1.3
2022
[55] Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering T. McDonald, ..., and Brenda Ng ArXiv 2022 - 3 citations - Show abstract - Cite - PDF 6.3% topic match
6.3%
0.1
2015
[56] A review of recent progress in multi document summarization S. Tabassum and Eugénio Oliveira Journal Not Provided 2015 - 1 citations - Show abstract - Cite 6.3% topic match
6.2%
0.4
2013
[57] Answering Questions from Multiple Documents – the Role of Multi-Document Summarization P. Bhaskar Recent Advances in Natural Language Processing 2013 - 5 citations - Show abstract - Cite - PDF 6.2% topic match
Focuses on multi-document summarization for question answering. Describes a system using document retrieval, multi-document summarization with graph-based clustering, and presents answers as fused summaries. Published in 2013, not within the required 2020 onwards window, thus not up-to-date for current MDQA advancements.
Focuses on multi-document summarization for question answering. Describes a system using document retrieval, multi-document summarization with graph-based clustering, and presents answers as fused summaries. Published in 2013, not within the required 2020 onwards window, thus not up-to-date for current MDQA advancements.
6.2%
0.7
2005
[58] Evaluating Summaries and Answers: Two Sides of the Same Coin? Jimmy J. Lin and Dina Demner-Fushman Journal Not Provided 2005 - 13 citations - Show abstract - Cite - PDF 6.2% topic match
Discusses the convergence between QA and multi-document summarization. Explores knowledge transfer between question answering and summarization for evaluating systems and satisfying user information needs. Published in 2005, thus not relevant for post-2020 MDQA trends and advancements.
Discusses the convergence between QA and multi-document summarization. Explores knowledge transfer between question answering and summarization for evaluating systems and satisfying user information needs. Published in 2005, thus not relevant for post-2020 MDQA trends and advancements.
6.0%
0.1
2008
[59] BioinQA Multidocument Question Answering System : Providing Access to E-learning for masses Sparsh Mittal, ..., and A. Mittal Journal Not Provided 2008 - 2 citations - Show abstract - Cite 6.0% topic match
Discusses a Multi-document Question Answering (MDQA) system for e-learning. Details the BioinQA system’s ability to compose answers from single and multiple documents. Published in 2008; outside the 2020 onwards publication window.
Discusses a Multi-document Question Answering (MDQA) system for e-learning. Details the BioinQA system’s ability to compose answers from single and multiple documents. Published in 2008; outside the 2020 onwards publication window.
5.7%
5.5
2023
[60] WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering Valeriia Bolotova-Baranova, ..., and M. Sanderson Annual Meeting of the Association for Computational Linguistics 2023 - 11 citations - Show abstract - Cite - PDF 5.7% topic match
5.5%
0.0
2014
[61] Abstractive Multi-Document Summarization: An Overview D. Y. Sakhare and Dr. Rajkumar Journal Not Provided 2014 - 0 citations - Show abstract - Cite 5.5% topic match
5.5%
5.9
2022
[62] Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization Artidoro Pagnoni, ..., and Chien-Sheng Wu ArXiv 2022 - 12 citations - Show abstract - Cite - PDF 5.5% topic match
5.1%
3.0
2016
[63] Identifying Multidocument Relations E. Maziero, ..., and T. Pardo International Workshop on Natural Language Processing and Cognitive Science 2016 - 24 citations - Show abstract - Cite 5.1% topic match
5.1%
1.1
2018
[64] Survey on Extractive Text Summarization Methods with Multi-Document Datasets K. P.N.Varalakshmi and Jagadish S. Kallimani 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2018 - 7 citations - Show abstract - Cite 5.1% topic match
5.0%
10.7
2023
[65] Retrieval-Generation Synergy Augmented Large Language Models Zhangyin Feng, ..., and Bing Qin ArXiv 2023 - 13 citations - Show abstract - Cite - PDF 5.0% topic match
4.9%
0.1
2009
[66] Supervised Approaches to Complex Question Answering Yllias Chali and Shafiq R. Joty Journal Not Provided 2009 - 2 citations - Show abstract - Cite 4.9% topic match
4.8%
4.1
2021
[67] TWEAC: Transformer with Extendable QA Agent Classifiers Gregor Geigle, ..., and Iryna Gurevych ArXiv 2021 - 15 citations - Show abstract - Cite - PDF 4.8% topic match
4.6%
0.8
2016
[68] A Query-Based Summarization Service from Multiple News Sources Elaheh Shafieibavani, ..., and Fang Chen 2016 IEEE International Conference on Services Computing (SCC) 2016 - 7 citations - Show abstract - Cite 4.6% topic match
4.5%
0.3
2007
[69] The Implementation of a Query-Directed Multi-document Summarization System Tingting He, ..., and P. Hu Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007) 2007 - 6 citations - Show abstract - Cite 4.5% topic match
3.8%
0.5
2010
[70] Topic aspect analysis for multi-document summarization Chao Shen, ..., and Tao Li Proceedings of the 19th ACM international conference on Information and knowledge management 2010 - 7 citations - Show abstract - Cite 3.8% topic match
3.5%
0.0
1999
[71] Automatic Text Summarization of Multiple Documents Thesis Proposal Thesis Committee Vibhu Mittal, ..., and Jan O. Pedersen Journal Not Provided 1999 - 0 citations - Show abstract - Cite 3.5% topic match
3.3%
0.0
2024
[72] PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering Yihao Ding, ..., and S. Han ArXiv 2024 - 0 citations - Show abstract - Cite - PDF 3.3% topic match
3.0%
0.6
2023
[73] Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks Kanishka Misra, ..., and Siamak Shakeri Annual Meeting of the Association for Computational Linguistics 2023 - 1 citations - Show abstract - Cite - PDF 3.0% topic match
3.0%
1.5
2011
[74] Query Snowball: A Co-occurrence-based Approach to Multi-document Summarization for Question Answering Hajime Morita, ..., and M. Okumura Annual Meeting of the Association for Computational Linguistics 2011 - 20 citations - Show abstract - Cite - PDF 3.0% topic match
2.8%
0.0
2022
[75] Structured Knowledge Grounding for Question Answering Yujie Lu, ..., and Kairui Zhou Journal Not Provided 2022 - 0 citations - Show abstract - Cite - PDF 2.8% topic match
2.7%
0.0
2022
[76] Towards Multi-Hop Open-Domain Question Answering by Dense Retrieval Zhao Meng Prof and Dr. Roger Wattenhofer Journal Not Provided 2022 - 0 citations - Show abstract - Cite 2.7% topic match
2.5%
1.4
2019
[77] Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models W. Cho, ..., and Sungjin Lee Conference on Empirical Methods in Natural Language Processing 2019 - 7 citations - Show abstract - Cite - PDF 2.5% topic match
2.4%
3.8
2021
[78] Learning to Generate Questions by Learning to Recover Answer-containing Sentences Seohyun Back, ..., and J. Choo Findings 2021 - 15 citations - Show abstract - Cite - PDF 2.4% topic match
2.4%
0.0
2024
[79] SEAM: A Stochastic Benchmark for Multi-Document Tasks Gili Lior, ..., and Gabriel Stanovsky Journal Not Provided 2024 - 0 citations - Show abstract - Cite - PDF 2.4% topic match
2.4%
15.0
2023
[80] Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering Yike Wu, ..., and Wei Song ArXiv 2023 - 19 citations - Show abstract - Cite - PDF 2.4% topic match
2.0%
0.5
2023
[81] Design Principles and a Software Reference Architecture for Big Data Question Answering Systems L. Moraes, ..., and C. D. Aguiar International Conference on Enterprise Information Systems 2023 - 1 citations - Show abstract - Cite 2.0% topic match
1.6%
2.6
2019
[82] Frustratingly Easy Natural Question Answering Lin Pan, ..., and Avirup Sil ArXiv 2019 - 14 citations - Show abstract - Cite - PDF 1.6% topic match
1.6%
0.0
2009
[83] Extensible Framework for Distinct Question Answering Agents Hyo-Jung Oh, ..., and Myung-Gil Jang 2009 International Conference on Knowledge and Systems Engineering 2009 - 0 citations - Show abstract - Cite 1.6% topic match
1.6%
26.2
2022
[84] ASQA: Factoid Questions Meet Long-Form Answers Ivan Stelmakh, ..., and Ming-Wei Chang ArXiv 2022 - 71 citations - Show abstract - Cite - PDF 1.6% topic match
1.2%
85.0
2017
[85] The NarrativeQA Reading Comprehension Challenge Tomás Kociský, ..., and Edward Grefenstette Transactions of the Association for Computational Linguistics 2017 - 597 citations - Show abstract - Cite - PDF 1.2% topic match
1.0%
34.8
2023
[86] L-Eval: Instituting Standardized Evaluation for Long Context Language Models Chen An, ..., and Xipeng Qiu ArXiv 2023 - 50 citations - Show abstract - Cite - PDF 1.0% topic match
0.9%
0.1
2006
[87] Query-focused multidocument summarization based on hybrid relevance analysis and surface feature salience Jen-Yuan Yeh, ..., and Wei-Pang Yang Journal Not Provided 2006 - 2 citations - Show abstract - Cite 0.9% topic match
0.8%
1.4
2020
[88] DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering Yuyu Zhang, ..., and Le Song ArXiv 2020 - 6 citations - Show abstract - Cite 0.8% topic match
0.7%
12.7
2023
[89] Merging Generated and Retrieved Knowledge for Open-Domain QA Yunxiang Zhang, ..., and Lu Wang ArXiv 2023 - 15 citations - Show abstract - Cite - PDF 0.7% topic match
0.7%
0.4
2003
[90] Using Background Information for Multi-document Summarization and Summaries in Response to a Question Atefeh Farzindar and G. Lapalme Journal Not Provided 2003 - 8 citations - Show abstract - Cite 0.7% topic match
0.5%
0.0
2019
[91] Machine learning based review on Development and Classification of Question-Answering Systems Sayli Uttarwar, ..., and Nikahat Mulla 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) 2019 - 0 citations - Show abstract - Cite 0.5% topic match
0.4%
2.7
2021
[92] Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review Ammar Arbaaeen and Asadullah Shah Inf. 2021 - 10 citations - Show abstract - Cite 0.4% topic match
0.4%
0.0
2024
[93] Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering Yuan Gao, ..., and Xinyu Dai ArXiv 2024 - 0 citations - Show abstract - Cite - PDF 0.4% topic match
0.3%
0.5
2019
[94] A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization Yuliska and T. Sakai 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT) 2019 - 3 citations - Show abstract - Cite 0.3% topic match
0.2%
10.4
2020
[95] QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines Valentina Pyatkin, ..., and Ido Dagan ArXiv 2020 - 44 citations - Show abstract - Cite - PDF 0.2% topic match
0.2%
0.2
2012
[96] A Novel Biased Diversity Ranking Model for Query-Oriented Multi-Document Summarization Kai Lei and Yin Zeng Applied Mechanics and Materials 2012 - 2 citations - Show abstract - Cite 0.2% topic match
0.2%
0.8
2019
[97] Document-Based Question Answering Improves Query-Focused Multi-document Summarization W. Li, ..., and Ming Zhou Natural Language Processing and Chinese Computing 2019 - 4 citations - Show abstract - Cite 0.2% topic match
0.2%
1.6
2021
[98] HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles O. Boni, ..., and D. Konopnicki ArXiv 2021 - 5 citations - Show abstract - Cite - PDF 0.2% topic match
0.2%
22.3
2023
[99] ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent Renat Aksitov, ..., and Sanjiv Kumar ArXiv 2023 - 23 citations - Show abstract - Cite - PDF 0.2% topic match
0.2%
0.0
2023
[100] Model Analysis & Evaluation for Ambiguous Question Answering Konstantinos Papakostas and Irene Papadopoulou Annual Meeting of the Association for Computational Linguistics 2023 - 0 citations - Show abstract - Cite - PDF 0.2% topic match
0.1%
5.0
2023
[101] KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph Tiezheng Guo, ..., and Yingyou Wen ArXiv 2023 - 5 citations - Show abstract - Cite - PDF 0.1% topic match
0.1%
0.2
2019
[102] Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal Alex Long, ..., and Wei Wang ArXiv 2019 - 1 citations - Show abstract - Cite - PDF 0.1% topic match
0.1%
0.4
2016
[103] Ontology and Query-Focused Multi-Document Summarization System Mr. K. Yogeswara Rao and D. N. Rao Journal Not Provided 2016 - 4 citations - Show abstract - Cite 0.1% topic match
0.1%
1.2
2024
[104] Domain Adaptation and Summary Distillation for Unsupervised Query Focused Summarization Jiancheng Du and Yang Gao IEEE Transactions on Knowledge and Data Engineering 2024 - 1 citations - Show abstract - Cite 0.1% topic match
0.1%
0.0
2023
[105] Adapting Pre-trained Generative Models for Extractive Question Answering Prabir Mallick, ..., and Indrajit Bhattacharya ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 0.1% topic match
0.1%
11.4
2022
[106] ProQA: Structural Prompt-based Pre-training for Unified Question Answering Wanjun Zhong, ..., and Nan Duan North American Chapter of the Association for Computational Linguistics 2022 - 30 citations - Show abstract - Cite - PDF 0.1% topic match
0.1%
2.6
2024
[107] Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs Lihui Liu, ..., and Hanghang Tong ArXiv 2024 - 2 citations - Show abstract - Cite - PDF 0.1% topic match
0.1%
12.3
2023
[108] Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs Chao Feng, ..., and Zichu Fei ArXiv 2023 - 16 citations - Show abstract - Cite - PDF 0.1% topic match
0.0%
4.0
2021
[109] Complex Question Answering on knowledge graphs using machine translation and multi-task learning Saurabh Srivastava, ..., and Gautam M. Shroff Conference of the European Chapter of the Association for Computational Linguistics 2021 - 16 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
5.4
2022
[110] How Do We Answer Complex Questions: Discourse Structure of Long-form Answers Fangyuan Xu, ..., and Eunsol Choi ArXiv 2022 - 15 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2023
[111] Research on Intelligent Question-Answering Systems Based on Large Language Models and Knowledge Graphs Qinglin Wu and Yan Wang 2023 16th International Symposium on Computational Intelligence and Design (ISCID) 2023 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
7.0
2024
[112] KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph Jinhao Jiang, ..., and Ji-Rong Wen ArXiv 2024 - 6 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
3.2
2020
[113] QURIOUS: Question Generation Pretraining for Text Generation Shashi Narayan, ..., and Ryan T. McDonald ArXiv 2020 - 15 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
17.6
2023
[114] Graph Neural Prompting with Large Language Models Yijun Tian, ..., and Panpan Xu AAAI Conference on Artificial Intelligence 2023 - 22 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.7
2023
[115] EpiK-Eval: Evaluation for Language Models as Epistemic Models Gabriele Prato, ..., and Sarath Chandar Conference on Empirical Methods in Natural Language Processing 2023 - 2 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
9.0
2019
[116] ReQA: An Evaluation for End-to-End Answer Retrieval Models Amin Ahmad, ..., and Daniel Matthew Cer Conference on Empirical Methods in Natural Language Processing 2019 - 49 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2022
[117] Flexible and Structured Knowledge Grounded Question Answering Yujie Lu, ..., and Kairui Zhou ArXiv 2022 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2020
[118] C HEMISTRY QA: A C OMPLEX Q UESTION A NSWERING D ATASET FROM C HEMISTRY No author found Journal Not Provided 2020 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2024
[119] Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models Huanxuan Liao, ..., and Jun Zhao ArXiv 2024 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.1
2023
[120] Visual Explanation for Open-Domain Question Answering With BERT Zekai Shao, ..., and Siming Chen IEEE Transactions on Visualization and Computer Graphics 2023 - 2 citations - Show abstract - Cite 0.0% topic match
0.0%
0.9
2023
[121] Towards leveraging LLMs for Conditional QA Syed-Amad Hussain, ..., and Preethi Raghavan ArXiv 2023 - 1 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
9.1
2023
[122] keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM Chaojie Wang, ..., and Bo An ArXiv 2023 - 9 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2006
[123] Summarizing relevant information for question-answering using hybrid relevance analysis and surface feature salience Jen-Yuan Yeh, ..., and Wei-Pang Yang WSEAS Transactions on Information Science and Applications archive 2006 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
1.2
2024
[124] Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering Mingxu Tao, ..., and Yansong Feng ArXiv 2024 - 1 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.8
2009
[125] Query-Focused Summaries or Query-Biased Summaries? Rahul Katragadda and Vasudeva Varma Annual Meeting of the Association for Computational Linguistics 2009 - 12 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
2.1
2016
[126] Question Answering on Linked Data: Challenges and Future Directions Saeedeh Shekarpour, ..., and C. Lange Proceedings of the 25th International Conference Companion on World Wide Web 2016 - 19 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2024
[127] G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning Ruiting Dai, ..., and Yao Cheng Proceedings of the 2024 International Conference on Multimedia Retrieval 2024 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
49.4
2023
[128] Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation Ruiyang Ren, ..., and Haifeng Wang ArXiv 2023 - 71 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.5
2022
[129] Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness Dan Su ArXiv 2022 - 1 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.0
2009
[130] Automatic Summarization from Multiple Documents Georgios Giannakopoulos https://doi.org/10.12681/EADD/18012 2009 - 16 citations - Show abstract - Cite 0.0% topic match
0.0%
13.0
2018
[131] Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models Tal Baumel, ..., and Michael Elhadad ArXiv 2018 - 90 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
2.6
2018
[132] CQASUMM: Building References for Community Question Answering Summarization Corpora Tanya Chowdhury and Tanmoy Chakraborty Proceedings of the ACM India Joint International Conference on Data Science and Management of Data 2018 - 16 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
114.7
2021
[133] QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering Michihiro Yasunaga, ..., and J. Leskovec North American Chapter of the Association for Computational Linguistics 2021 - 425 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
36.2
2020
[134] Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps Xanh Ho, ..., and Akiko Aizawa ArXiv 2020 - 150 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2022
[135] Learning to Answer Multilingual and Code-Mixed Questions D. Gupta ArXiv 2022 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.7
2008
[136] A Document Graph Based Query Focused Multi-Document Summarizer Sibabrata Paladhi and Sivaji Bandyopadhyay International Joint Conference on Natural Language Processing 2008 - 12 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.7
2024
[137] Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top Keyuan Cheng, ..., and Di Wang ArXiv 2024 - 1 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.7
2010
[138] Focused multi-document summarization: human summarization activity vs. automated systems techniques Quinsulon Israel, ..., and I. Song Journal of Computing Sciences in Colleges 2010 - 10 citations - Show abstract - Cite 0.0% topic match
0.0%
31.7
2023
[139] A Critical Evaluation of Evaluations for Long-form Question Answering Fangyuan Xu, ..., and Eunsol Choi ArXiv 2023 - 50 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
12.5
2019
[140] Capturing Greater Context for Question Generation Anh Tuan Luu, ..., and R. Barzilay ArXiv 2019 - 65 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
3.2
2020
[141] NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets Victor C. Dibia ArXiv 2020 - 14 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2010
[142] An Approach to Query-focused Multi-Document Summarization Cai Dong-feng Journal of Chinese information processing 2010 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
0.3
2013
[143] Query Focused Language Independent Multi-Document Summarization P. Bhaskar Journal Not Provided 2013 - 3 citations - Show abstract - Cite 0.0% topic match
0.0%
41.0
2020
[144] Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering Yanlin Feng, ..., and Xiang Ren ArXiv 2020 - 191 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
4.4
2023
[145] Question Answering as Programming for Solving Time-Sensitive Questions Xinyu Zhu, ..., and Yujiu Yang ArXiv 2023 - 7 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2024
[146] Multi-hop Question Answering over Knowledge Graphs using Large Language Models Abir Chakraborty ArXiv 2024 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.9
2017
[147] Automatic Generation of Review Matrices as Multi-document Summarization of Scientific Papers Hayato Hashimoto, ..., and Akiko Aizawa Journal Not Provided 2017 - 7 citations - Show abstract - Cite 0.0% topic match
0.0%
0.3
2008
[148] Personalized multi-document summarization in information retrieval Xiaoyin Yang and Xiao-Rong Liu 2008 International Conference on Machine Learning and Cybernetics 2008 - 5 citations - Show abstract - Cite 0.0% topic match
0.0%
2.7
2024
[149] Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs? Wangyue Li, ..., and Noa Garcia ArXiv 2024 - 2 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
64.7
2017
[150] Constructing Datasets for Multi-hop Reading Comprehension Across Documents Johannes Welbl, ..., and Sebastian Riedel Transactions of the Association for Computational Linguistics 2017 - 465 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.8
2003
[151] A survey for Multi-Document Summarization S. Sekine and Chikashi Nobata https://doi.org/10.3115/1119467.1119476 2003 - 38 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.2
2019
[152] Bend but Don’t Break? Multi-Challenge Stress Test for QA Models Hemant Pugaliya, ..., and Eric Nyberg Conference on Empirical Methods in Natural Language Processing 2019 - 6 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2023
[153] Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation Nishant Balepur, ..., and K. Chang ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
2.6
2019
[154] Advances in Natural Language Question Answering: A Review K. S. D. Ishwari, ..., and Y. Mallawarachchi ArXiv 2019 - 15 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
2.3
2021
[155] Weakly Supervised Pre-Training for Multi-Hop Retriever Yeon Seonwoo, ..., and Alice H. Oh Findings 2021 - 8 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2023
[156] Constructing a Closed-Domain Question Answering System with Generative Language Models Hung Le, ..., and Shogo Okada 2023 15th International Conference on Knowledge and Systems Engineering (KSE) 2023 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
1.7
2004
[157] Multi-answer-focused multi-document summarization using a question-answering engine Tatsunori Mori, ..., and Yoshiaki Asada ACM Trans. Asian Lang. Inf. Process. 2004 - 34 citations - Show abstract - Cite 0.0% topic match
0.0%
5.6
2024
[158] Beyond the Answers: Reviewing the Rationality of Multiple Choice Question Answering for the Evaluation of Large Language Models Hao Wang, ..., and Ting Liu ArXiv 2024 - 5 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.1
2017
[159] Resúmenes de múltiples documentos guiados por consulta empleando representaciones distribucionales Leticia Luna-Tlatelpa, ..., and Carlos J. Rivero-Moreno Res. Comput. Sci. 2017 - 1 citations - Show abstract - Cite 0.0% topic match
0.0%
0.8
2014
[160] Question Answering: A Survey of Research, Techniques and Issues Vaishali Singh and S. Dwivedi Int. J. Inf. Retr. Res. 2014 - 8 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2023
[161] PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization Joseph Peper, ..., and Lu Wang ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
3.1
2023
[162] Investigating Answerability of LLMs for Long-Form Question Answering Meghana Moorthy Bhat, ..., and Semih Yavuz ArXiv 2023 - 4 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.7
2018
[163] HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph Somayeh Asadifar, ..., and Saeedeh Shekarpour ArXiv 2018 - 4 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2004
[164] An Experimental Study on Multi-Document Summarization for Question Answering Sanghee Choi and Young-Mee Chung Journal of The Korean Society for Information Management 2004 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
8.1
2023
[165] ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph Jinhao Jiang, ..., and Ji-Rong Wen Conference on Empirical Methods in Natural Language Processing 2023 - 8 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
60.3
2017
[166] Simple and Effective Multi-Paragraph Reading Comprehension Christopher Clark and Matt Gardner ArXiv 2017 - 432 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
0.0
2008
[167] Automatic Annotation Techniques for Supervised and Semi-supervised Query-focused Summarization Shafiq R. Joty Journal Not Provided 2008 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
0.8
2023
[168] SDbQfSum: Query‐focused summarization framework based on diversity and text semantic analysis Muhidin A. Mohamed, ..., and Victor I. Chang Expert Systems 2023 - 1 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2006
[169] Telugu-English Dictionary Based Cross Language Query Focused Multi-Document Summarization Prasad Pingali, ..., and Vasudeva Varma Journal Not Provided 2006 - 0 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2023
[170] Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach Pritom Saha Akash, ..., and Kevin Chen-Chuan Chang ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
1.5
2016
[171] Multi-document text summarization - a survey A. Tandel, ..., and Sujata Khedkar 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) 2016 - 13 citations - Show abstract - Cite 0.0% topic match
0.0%
9.1
2023
[172] Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation Yuanyuan Liang, ..., and Yunshi Lan ArXiv 2023 - 11 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
14.3
2023
[173] TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks Shubhra (Santu) Karmaker and Dongji Feng ArXiv 2023 - 23 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
8.8
2005
[174] Automatic Text Summarization of Newswire: Lessons Learned from the Document Understanding Conference A. Nenkova AAAI Conference on Artificial Intelligence 2005 - 171 citations - Show abstract - Cite 0.0% topic match
0.0%
1.0
2022
[175] LEPUS: Prompt-based Unsupervised Multi-hop Reranking for Open-domain QA Muhammad Khalifa, ..., and Lu Wang ArXiv 2022 - 3 citations - Show abstract - Cite 0.0% topic match
0.0%
1.1
2020
[176] Interpretable Complex Question Answering Soumen Chakrabarti Proceedings of The Web Conference 2020 2020 - 5 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2024
[177] Large Language Models Can Better Understand Knowledge Graphs Than We Thought Xinbang Dai, ..., and Guilin Qi Journal Not Provided 2024 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
12.1
2022
[178] Self-Prompting Large Language Models for Open-Domain QA Junlong Li, ..., and Hai Zhao ArXiv 2022 - 36 citations - Show abstract - Cite 0.0% topic match
0.0%
0.0
2023
[179] Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games Yizhe Zhang, ..., and N. Jaitly Journal Not Provided 2023 - 0 citations - Show abstract - Cite - PDF 0.0% topic match
0.0%
26.1
2018
[180] Commonsense for Generative Multi-Hop Question Answering Tasks Lisa Bauer, ..., and Mohit Bansal Conference on Empirical Methods in Natural Language Processing 2018 - 164 citations - Show abstract - Cite - PDF 0.0% topic match

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