Abstractive Text Summarization using Attentive GRU based Encoder-Decoder
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Abstract (arXiv)
In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, namely, machine learning. Indeed automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to overcome the problem of handling long sequences in the input text. A news-summary dataset has been used to train the model. The output is observed to outperform competitive models in the literature. The generated summary can be used as a newspaper headline.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 25 Feb 2023.
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12 of 18 references.
References · 12
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