Sequence-to-Sequence Models w/ Conversational Structure for Abstractive Dialogue Summarization

EMNLP 2020

"Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization" is work conducted by Jiaao Chen and Diyi Yang at Georgia Tech. This work was accepted to the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 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 humanhuman/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 Multi-View-Seq2Seq. Chen and Yang are affiliated with the Machine Learning Center at Georgia Tech. About ML@GT The Machine Learning Center was founded in 2016 as an interdisciplinary research center (IRC) at the Georgia Institute of Technology. Since then, we have grown to include over 190 affiliated faculty members and 60 Ph.D. students, all publishing at world-renowned conferences. The center aims to research and develop innovative and sustainable technologies using machine learning and artificial intelligence (AI) that serve our community in socially and ethically responsible ways.