Abstractive Meeting Summarization Using Dependency Graph Fusion

By Siddhartha Banerjee, Prasenjit Mitra, Kazunari Sugiyama. 2015

27 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Summarization

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Citations in the corpus

SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (2019, methodology): “The challenges posed by the abstractive dialogue summarization task have been discussed in the literature with regard to AMI meeting corpus (McCowan et al., 2005), e.g. Banerjee et al. (2015), Mehdad et al. (2014), Goo and Chen (2018).”
Abstractive Summarization of Spoken and Written Conversation (2019, methodology): “Banerjee et al. (2016) uses the dialogue structure directly to generate summary sentences .”