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pro vyhledávání: '"neural abstractive summarization"'
Akademický článek
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Autor:
Cao, Meng
Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summarization methods can be roughly di
Externí odkaz:
http://arxiv.org/abs/2204.09519
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output an
Externí odkaz:
http://arxiv.org/abs/2010.08014
This study develops a calibrated beam-based algorithm with awareness of the global attention distribution for neural abstractive summarization, aiming to improve the local optimality problem of the original beam search in a rigorous way. Specifically
Externí odkaz:
http://arxiv.org/abs/2009.06891
An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In this work, we analyze summarization decoders in both blackbox and white
Externí odkaz:
http://arxiv.org/abs/2010.07882
We present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide a sufficient amount of training data enough to meet the requirement o
Externí odkaz:
http://arxiv.org/abs/2010.03722
Attentional, RNN-based encoder-decoder architectures have achieved impressive performance on abstractive summarization of news articles. However, these methods fail to account for long term dependencies within the sentences of a document. This proble
Externí odkaz:
http://arxiv.org/abs/2004.09739
Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization. Nevertheless, existing neural abstractive systems frequently generate factually incorrect summaries and are vulnerable to adversarial informat
Externí odkaz:
http://arxiv.org/abs/1810.06065
Publikováno v:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observ
Externí odkaz:
http://arxiv.org/abs/1806.05504
Autor:
Hua, Xinyu, Wang, Lu
We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neur
Externí odkaz:
http://arxiv.org/abs/1707.07062