Learning to Rank Utterances for Query-Focused Meeting Summarization
Xingxian Liu Yajing Xu
Query-focused meeting summarization(QFMS) aims to generate a specific summary
for the given query according to the meeting transcripts. Due to the conflict
between long meetings and limited input size, previous works mainly adopt
extract-then-summarize methods, which use extractors to simulate binary labels
or ROUGE scores to extract utterances related to the query and then generate a
summary. However, the previous approach fails to fully use the comparison
between utterances. To the extractor, comparison orders are more important than
specific scores. In this paper, we propose a Ranker-Generator framework. It
learns to rank the utterances by comparing them in pairs and learning from the
global orders, then uses top utterances as the generator's input. We show that
learning to rank utterances helps to select utterances related to the query
effectively, and the summarizer can benefit from it. Experimental results on
QMSum show that the proposed model outperforms all existing multi-stage models
with fewer parameters.