A Novel Model based on Encoder-Decoder Architecture and Attention Mechanism for Automatic Abstractive Text Summarization
Subject Areas :hasan aliakbarpor 1 , mohammadtaghi manzouri 2 , amirmasoud rahmani 3
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Keywords: Deep learning, Abstractive summarization, Encoder-decoder architecture, Auxiliary attention mechanism, Linguistic features,
Abstract :
By the extension of the Web and the availability of a large amount of textual information, the development of automatic text summarization models as an important aspect of natural language processing has attracted many researchers. However, with the growth of deep learning methods in the field of text processing, text summarization has also entered a new phase of development and abstractive text summarization has experienced significant progress in recent years. Even though, it can be claimed that all the potential of deep learning has not been used for this aim and the need for progress in this field, as well as considering the human cognition in creating the summarization model, is still felt. In this regard, an encoder-decoder architecture equipped with auxiliary attention is proposed in this paper which not only used the combination of linguistic features and embedding vectors as the input of the learning model but also despite previous studies that commonly employed the attention mechanism in the decoder, it utilized auxiliary attention mechanism in the encoder to imitate human brain and cognition in summary generation. By the employment of the proposed attention mechanism, only the most important parts of the text rather than the whole input text are encoded and then sent to the decoder to generate the summary. The proposed model also used a switch with a threshold in the decoder to overcome the rare words problem. The proposed model was examined on CNN / Daily Mail and DUC-2004 datasets. Based on the empirical results and according to the ROUGE evaluation metric, the proposed model obtained a higher accuracy compared to other existing methods for generating abstractive summaries on both datasets.
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