Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow
By Saadia Gabriel, Antoine Bosselut, Ari Holtzman, Kyle Lo, Asli Celikyilmaz, Yejin Choi. 2019
11 citations recorded by the source corpus.
We introduce Cooperative Generator-Discriminator Networks (Co-opNet), a general framework for abstractive summarization with distinct modeling of the narrative flow in the output summary. Most current approaches to abstractive summarization, in contrast, are based on datasets whose target summaries are either a single sentence, or a bag of standalone sentences (e.g., extracted highlights of a story), neither of which allows for learning coherent narrative flow in the output summaries. To promote research toward abstractive summarization with narrative flow, we first introduce a new dataset, Scientific Abstract SummarieS (SASS), where the abstracts are used as proxy gold summaries for scientific articles. We then propose Co-opNet, a novel transformer-based framework where the generator works with the discourse discriminator to compose a long-form summary. Empirical results demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.
Subfields: Artificial Intelligence
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Jason Aaron Edward Weston
- Y-Lan Boureau
- Mohit Bansal
- Luke S. Zettlemoyer
- Sean Welleck
- Kyunghyun Cho
- Hannaneh Hajishirzi
Citations in the corpus
- Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training (2019, background): “The work of Gabriel et al. (2019) has also studied improving narrative flow with a discriminative rescorer, but in that case for generated language.”
- Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences (2020, methodology): “The proposed CoE algorithms, on the other hand, are closely related to rescoring methods employed in NLG, including work by (Holtzman et al., 2018; Cho et al., 2019; Gabriel et al., 2019; Hossain et al., 2020; Goldfarb-Tarrant et al., 2020), among others.”