DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

By Yulong Chen, Yang Liu, Liang Chen, Yue Zhang. Findings, 2021

326 citations recorded by the source corpus.

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8d2891737045f3610c027997b8b58705899e8922

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In Artificial Intelligence

Citations in the corpus

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).”
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023, methodology): “, 2018) to summarize articles in the news domain, while the DialogSUM (Chen et al., 2021b) and SAMSum (Gliwa et al.”
DialSummEval: Revisiting Summarization Evaluation for Dialogues (2022, methodology): “, 2019), DialogSum (Chen et al., 2021) and MediaSum (Zhu et al.”
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022, methodology): “We use the evaluation ratings released in the DSTC-10 Automatic evaluation challenge (Chen et al., 2021b) that consists of 65,938 context-response pairs along with corresponding human ratings aggregated across various evaluation sets.”
VScript: Controllable Script Generation with Visual Presentation (2022, unknown): “We ﬁne-tune DialoGPT -large (Zhang et al., 2020) on the inversed SAMSum and DialogSum Corpus, where the model generates dialogue turn-by-turn iteratively.”