Papers by Konstantin Chernis

1 papers
SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)

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Challenge: Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used.
Approach: They propose a low-extractive corpus of movie dialogues for abstractive text summarization . they use an alignment algorithm to construct the corpus and a baseline evaluation .
Outcome: The proposed method is low-extractive and shows high performance in dialogue datasets.

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