Abstract
Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the encoder to optimize the encoding information at the global level and effectively improves the memory capacity of conventional LSTM. Inspired by human coarse-to-fine understanding mode, localness is modeled by Gaussian bias to improve contextualization for each sentence, and merged into the self-matching energy. The refined self-matching mechanism not only establishes global document attention but perceives association with neighboring signals. At the decoder side, the pointer network is utilized to perform a two-hop attention on context and extraction state. Evaluations on the CNN/Daily Mail dataset verify that the proposed model outperforms the strong baseline models and statistical significantly.
Highlights
Automatic summarization systems have been made great progress in many applications, such as headline generation [1], single or multi-document summarization [2,3], opinion mining [4], text categorization, etc
The abstractive summarization is more difficult as it has to deal with factual or grammatical errors, semantic incoherence, as well as problems with the obtaining of explicit textual paraphrases and generalizations. Extractive methods relieve these problems by identifying important sentences from the document, summary generated by extractive methods are generally better than that generated by abstractive methods in terms of grammaticality and factuality
All of our recall-oriented understanding for gisting evaluation (ROUGE) scores are reported by the official ROUGE script, with a 95% confidence interval of at most
Summary
Automatic summarization systems have been made great progress in many applications, such as headline generation [1], single or multi-document summarization [2,3], opinion mining [4], text categorization, etc. The abstractive summarization is more difficult as it has to deal with factual or grammatical errors, semantic incoherence, as well as problems with the obtaining of explicit textual paraphrases and generalizations. Extractive methods relieve these problems by identifying important sentences from the document, summary generated by extractive methods are generally better than that generated by abstractive methods in terms of grammaticality and factuality. Those methods may encounter problems like the lack of core information and incomprehensive generalization. With the advantages of simpler calculation and higher generation efficiency, numerous empirical comparisons in recent years have shown that the state-of-the-art extractive methods usually have better performance than the abstractive ones [5]
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