Controllable Abstractive Dialogue Summarization with Sketch Supervision
Chien-Sheng Wu, Linqing Liu, Wenhao Liu, Pontus Stenetorp, Caiming Xiong · 2021
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-theart 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 humanannotated summaries.