ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

Alexander Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev


Abstract
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues–viewpoints–assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.
Anthology ID:
2021.acl-long.535
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6866–6880
Language:
URL:
https://aclanthology.org/2021.acl-long.535
DOI:
10.18653/v1/2021.acl-long.535
Bibkey:
Cite (ACL):
Alexander Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, and Dragomir Radev. 2021. ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6866–6880, Online. Association for Computational Linguistics.
Cite (Informal):
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining (Fabbri et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.535.pdf
Video:
 https://aclanthology.org/2021.acl-long.535.mp4
Code
 Yale-LILY/ConvoSumm
Data
ConvoSummCNN/Daily MailCQASUMMNew York Times Annotated CorpusSAMSum