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MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

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arxiv 2103.06410 v2 pith:63O2OXOW submitted 2021-03-11 cs.CL

Chenguang Zhu , Yang Liu , Jie Mei , Michael Zeng This is my paper · ORCID
classification cs.CL
keywords datasetdialogueinterviewmediasumsummarizationtranscriptslarge-scalemedia
verification ladder T0 review T1 audit T2 compute T3 formal
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MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model's performance on other dialogue summarization tasks.

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  1. Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation

    cs.CL 2025-02 conditional novelty 5.0 of 10

    MADISSE assigns LLM evaluators random initial stances (faithful or unfaithful), has them debate in rounds, and adjudicates, improving summary faithfulness evaluation accuracy while introducing an annotated 'ambiguity'...

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