Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning

TL;DR

This paper introduces a novel syntax-aware multi-task learning approach for dialogue summarization that leverages speaker-specific linguistic styles to improve summary quality, demonstrating significant gains on the SAMSum dataset.

Contribution

It is the first to apply multi-task learning with syntactic information to enhance dialogue summarization performance.

Findings

Improved summarization accuracy over baseline models.

Leveraged POS tagging to distinguish speaker styles.

Analyzed cost-benefit trade-offs of the proposed method.

Abstract

Abstractive dialogue summarization is a challenging task for several reasons. First, most of the important pieces of information in a conversation are scattered across utterances through multi-party interactions with different textual styles. Second, dialogues are often informal structures, wherein different individuals express personal perspectives, unlike text summarization, tasks that usually target formal documents such as news articles. To address these issues, we focused on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. Therefore, we constructed a syntax-aware model by leveraging linguistic information (i.e., POS tagging), which alleviates the above issues by inherently distinguishing sentences uttered from individual speakers. We employed multi-task learning of both syntax-aware information and dialogue summarization. To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task. Experiments on a SAMSum corpus (a large-scale dialogue summarization corpus) demonstrated that our method improved upon the vanilla model. We further analyze the costs and benefits of our approach relative to baseline models.