Papers with multi-view sequence-to-sequence model

1 papers
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization (2020.emnlp-main)

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Challenge: Existing studies on text summarization focus on single-speaker docs, scientific publications and encyclopedia articles.
Approach: They propose a multi-view sequence-to-sequence model that extracts conversational structures from unstructured daily chats and incorporates different views to generate dialogue summaries.
Outcome: The proposed model outperforms state-of-the-art models via automatic evaluation and human judgment on a large-scale dialogue summarization corpus.

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