Controllable Abstractive Dialogue Summarization with Sketch Supervision

• URL: http://arxiv.org/abs/2105.14064v1
• Date: Fri, 28 May 2021 19:05:36 GMT
• Title: Controllable Abstractive Dialogue Summarization with Sketch Supervision
• Authors: Chien-Sheng Wu and Linqing Liu and Wenhao Liu and Pontus Stenetorp and Caiming Xiong
• Abstract summary: Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score.
• Score: 56.59357883827276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary. This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser. 2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case study and show competitive human evaluation results and controllability to human-annotated summaries.

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