Joint learning of answer selection and answer summary generation in community question answering

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve...

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Main Authors: DENG, Yang, LAM, Wai, XIE, Yuexiang, CHEN, Daoyuan, LI, Yaliang, YANG, Min, SHEN, Ying
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/9102
https://ink.library.smu.edu.sg/context/sis_research/article/10105/viewcontent/6266_Article_Text_9491_1_10_20200516.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-101052024-08-01T15:04:15Z Joint learning of answer selection and answer summary generation in community question answering DENG, Yang LAM, Wai XIE, Yuexiang CHEN, Daoyuan LI, Yaliang YANG, Min SHEN, Ying Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-Answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-Answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-ofthe-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries. 2020-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9102 info:doi/10.1609/aaai.v34i05.6266 https://ink.library.smu.edu.sg/context/sis_research/article/10105/viewcontent/6266_Article_Text_9491_1_10_20200516.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Community question answering Joint learning Question-answer pairs Summary generation Text summarization Databases and Information Systems Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Community question answering
Joint learning
Question-answer pairs
Summary generation
Text summarization
Databases and Information Systems
Information Security
spellingShingle Community question answering
Joint learning
Question-answer pairs
Summary generation
Text summarization
Databases and Information Systems
Information Security
DENG, Yang
LAM, Wai
XIE, Yuexiang
CHEN, Daoyuan
LI, Yaliang
YANG, Min
SHEN, Ying
Joint learning of answer selection and answer summary generation in community question answering
description Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-Answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-Answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-ofthe-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.
format text
author DENG, Yang
LAM, Wai
XIE, Yuexiang
CHEN, Daoyuan
LI, Yaliang
YANG, Min
SHEN, Ying
author_facet DENG, Yang
LAM, Wai
XIE, Yuexiang
CHEN, Daoyuan
LI, Yaliang
YANG, Min
SHEN, Ying
author_sort DENG, Yang
title Joint learning of answer selection and answer summary generation in community question answering
title_short Joint learning of answer selection and answer summary generation in community question answering
title_full Joint learning of answer selection and answer summary generation in community question answering
title_fullStr Joint learning of answer selection and answer summary generation in community question answering
title_full_unstemmed Joint learning of answer selection and answer summary generation in community question answering
title_sort joint learning of answer selection and answer summary generation in community question answering
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/9102
https://ink.library.smu.edu.sg/context/sis_research/article/10105/viewcontent/6266_Article_Text_9491_1_10_20200516.pdf
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