Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

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.

Keywords

Context modeling, Factoid questions, Multi-level interactions, On currents, Question Answering, Sentence level, Unified Modeling, Word level

Discipline

Databases and Information Systems | Information Security

Research Areas

Data Science and Engineering; Information Systems and Management

Areas of Excellence

Digital transformation

Publication

Proceedings of the 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, Online, 2020 July 25-30

First Page

1693

Last Page

1696

ISBN

9781450380164

Identifier

10.1145/3397271.3401208

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3397271.3401208

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