In this paper, we present an automatic abstractive summarization system of meeting conversations. Our system extends a novel multi-sentence fusion algorithm in order to generate abstract templates. It also leverages the relationship between summaries and their source meeting transcripts to select the best templates for generating abstractive summaries of meetings. Our manual and automatic evaluation results demonstrate the success of our system in achieving higher scores both in readability and informativeness.
REF employs multi-sentence fusion to construct summarization templates for meetings, leading to summaries with higher readability and informativeness.
A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships
INLG
Computer Science