Abstractive Meeting Summarization UsingDependency Graph Fusion

TL;DR

This paper introduces an abstractive meeting summarization method that fuses key utterances within discussion segments using integer linear programming, resulting in more informative summaries than extractive baselines.

Contribution

It presents a novel approach combining dependency graph fusion and integer linear programming for abstractive meeting summarization, improving summary informativeness.

Findings

Generated summaries are more informative than baselines.

The method effectively fuses content from multiple utterances.

Experimental results demonstrate improved summary quality.

Abstract

Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries. To improve this, we propose an approach to generate abstractive summaries by fusing important content from several utterances. Any meeting is generally comprised of several discussion topic segments. For each topic segment within a meeting conversation, we aim to generate a one sentence summary from the most important utterances using an integer linear programming-based sentence fusion approach. Experimental results show that our method can generate more informative summaries than the baselines.