Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Jiaao Chen, Diyi Yang


Abstract
Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations—an essential part of human-human/machine interaction where most important pieces of information are scattered across various utterances of different speakers—remains relatively under-investigated. This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/Multi-View-Seq2Seq.
Anthology ID:
2020.emnlp-main.336
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4106–4118
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.336
DOI:
10.18653/v1/2020.emnlp-main.336
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.336.pdf