8 papers with code • 0 benchmarks • 2 datasets
Generating a summary from meeting transcriptions.
These leaderboards are used to track progress in Meeting Summarization
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties.
We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
Lastly, we compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization.