Comprehensive and Evolution Study Focusing on Comparative Analysis of Automatic Text Summarization

Patel, R. D.; Thakkar, Amit; Makwana, Kamlesh; Patel, Jay

doi:10.1007/978-3-319-63645-0_43

Cited by 1 publication

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“…On the same dataset, See [41] proposed a pointer network and additionally used a loss term for the attention-coverage mechanism in the loss function of its model. Patel [37] studied on abstractive and extractive content rundown strategies. Kejun [22] proposed an improved word vector generation technique and an abstractive automatic summarization model.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

Automatic Text Summarization Using Deep Reinforcement Learning and Beyond

Sun

Wang

Zhao

2021

ITC

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In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.

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Section: Automatic Text Summarizationmentioning

confidence: 99%