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

By Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng. 2021

163 citations recorded by the source corpus.

This paper introduces 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.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (2023, methodology): “, 2018), MediaSum (Zhu et al., 2021), and Multi-News (Fabbri et al.”
LongT5: Efficient Text-To-Text Transformer for Long Sequences (2021, unknown): “…compare LongT5 with various top approaches: BigBird-PEGASUS (Zaheer et al., 2020b), HAT-BART (Rohde et al., 2021), DANCER PEGASUS (Gidiotis and Tsoumakas, 2020), PRIMER (Xiao et al., 2021), TG-MultiSum (Cui and Hu, 2021), LED (Beltagy et al., 2020), and an application of BART by Zhu et al. (2021).”
SummScreen: A Dataset for Abstractive Screenplay Summarization (2021, background): “…(SAMSum; Gliwa et al., 2019), podcasts (Clifton et al., 2020), meetings (AMI; Carletta et al., 2005; ICSI; Janin et al., 2003; QMSum; Zhong et al., 2021), livestreams (StreamHover; Cho et al., 2021), online forums (ForumSum; Khalman et al., 2021) and news interviews (MediaSum; Zhu et al., 2021).”
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (2023, methodology): “We use arXiv (Cohan et al., 2018), PubMed (Cohan et al., 2018), BigPatent (Sharma et al., 2019), Multi-News (Fabbri et al., 2019), MediaSum (Zhu et al., 2021), CNN / Daily Mail (Nallapati et al., 2016) dataset for the evaluation, measuring the performance with ROUGE-1/2/L metrics.”
DialSummEval: Revisiting Summarization Evaluation for Dialogues (2022, methodology): “With the proposal of dialogue summary datasets such as SAMSum (Gliwa et al., 2019), DialogSum (Chen et al., 2021) and Medi-aSum (Zhu et al., 2021), a number of models for automatic generation of dialogue summaries have emerged (Feng et al., 2021b; Liu and Chen, 2021; Zou et al., 2021; Qi et al.,…”
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2021, background): “…of dialogue summarization has attracted signiﬁcant research attentions (Mc-Cowan et al., 2005; Gliwa et al., 2019; Koay et al., 2020; Zhang et al., 2021; Zhong et al., 2021; Zhu et al., 2021; Chen et al., 2021a; Li et al., 2021; Chen et al., 2021c; Fabbri et al., 2021; Chen et al., 2021d).”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, methodology): “Dialogue summary covers several areas, including meetings [45, 80, 192], email threads [180], medical dialogues [40, 75, 146], customer service [96], and media interviews [191].”