Learning to Rank Utterances for Query-Focused Meeting Summarization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Learning-To-RankMeeting SummarizationSimilar Papers 제목 키워드 기반
Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a given meeting transcript conditioned upon a query. The main challenges for QFMS are the long input text length and sparse query-relevant informat…
Meeting SummarizationQuery-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization
Query-focused meeting summarization (QFMS) aims to generate summaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model th…
Meeting SummarizationRetrievalUnsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings
We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words. Concretely, a series of unsupervised topic models is explored and expe…
Decision MakingTopic ModelsDialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization
Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while i…
Data AugmentationMeeting SummarizationAbstractive Meeting Summarization UsingDependency Graph Fusion
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 fusin…
Meeting SummarizationSentenceSentence Fusion