short-paper

Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization

Authors:

Ying ShenAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

Pages 1693 - 1696

https://doi.org/10.1145/3397271.3401208

Published: 25 July 2020 Publication History

Abstract

Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the interaction between QA pairs in both word-level and sentence-level into the final question and answer representations. After that, we conduct the question-aware sequential extractor to produce a summary for the lengthy answer. Experimental results show that answer summarization benefits from both hierarchical and sequential context modeling and our method achieves superior performance on WikiHowQA and PubMedQA.

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In this paper, we bridge hierarchical and sequential context modeling for answer summarization to address the answer redundancy issue in non-factoid question answering.

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Cited By

Abazari Kia MGarifullina AKern MChamberlain JJameel S(2024)Question‐driven text summarization using an extractive‐abstractive frameworkComputational Intelligence10.1111/coin.1268940:3Online publication date: 24-Jun-2024
https://doi.org/10.1111/coin.12689
Park CKo Y(2024)Query-focused summarization with the context-graph information fusion transformerExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122699241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122699
Bai GHe SLiu KZhao J(2023)Bidirectional Sentence Ordering with Interactive DecodingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356151022:2(1-15)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3561510
Show More Cited By

Index Terms

Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
      2. Summarization

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Published In

cover image ACM Conferences

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

Copyright © 2020 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2020

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Shenzhen General Research Project
Research Grant Council of the Hong Kong Special Administrative Region, China

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SIGIR '20

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SIGIR

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Abazari Kia MGarifullina AKern MChamberlain JJameel S(2024)Question‐driven text summarization using an extractive‐abstractive frameworkComputational Intelligence10.1111/coin.1268940:3Online publication date: 24-Jun-2024
https://doi.org/10.1111/coin.12689
Park CKo Y(2024)Query-focused summarization with the context-graph information fusion transformerExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122699241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122699
Bai GHe SLiu KZhao J(2023)Bidirectional Sentence Ordering with Interactive DecodingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356151022:2(1-15)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3561510
Li BYang PZhao HZhang PLiu Z(2023)Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer SummarizationACM Transactions on Information Systems10.1145/351189141:1(1-27)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3511891
Wang XWang JXu BLin HZhang BYang Z(2022)Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and ValidationJMIR Medical Informatics10.2196/3805210:8(e38052)Online publication date: 15-Aug-2022
https://doi.org/10.2196/38052
Deng YLi YZhang WDing BLam W(2022)Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference ModelingACM Transactions on Information Systems10.1145/350778240:4(1-28)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3507782
Park CKo YAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)QSG TransformerProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531901(2589-2594)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531901
Deng YXie YLi YYang MLam WShen Y(2021)Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with KnowledgeACM Transactions on Information Systems10.1145/345753340:1(1-33)Online publication date: 8-Sep-2021
https://dl.acm.org/doi/10.1145/3457533

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