Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2020
Abstract
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.
Keywords
Community question answering, Joint learning, Question-answer pairs, Summary generation, Text summarization
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 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020 Feb 7-12
First Page
7651
Last Page
7658
ISBN
9781577358350
Identifier
10.1609/aaai.v34i05.6266
Publisher
AAAI Press
City or Country
Washington
Citation
DENG, Yang; LAM, Wai; XIE, Yuexiang; CHEN, Daoyuan; LI, Yaliang; YANG, Min; and SHEN, Ying.
Joint learning of answer selection and answer summary generation in community question answering. (2020). Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020 Feb 7-12. 7651-7658.
Available at: https://ink.library.smu.edu.sg/sis_research/9102
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v34i05.6266