Abstract
Abstractive text summarization is the task of generating the summary of text documents like humans do. It’s completely laborious and time taking process to summarize the lengthy documents manually. Abstractive text summarization takes a document as input and produces a summary by combining a piece of information from different source sentences and paraphrase them while maintaining the overall meaning of the document. Here, the abstractive text summarization task is done using various sequence-to-sequence (seq2seq) models and their performance has been analyzed on the MSMO dataset. Seq2seq with attention, pointer generator network (PGN) and pointer generator with coverage models are used to generate the summary. To improve accuracy, hyper-parameters were tuned and successfully obtained good results. ROUGE and BLEU scores are used to evaluate the performance of these models. Seq2seq with attention, PGN, and PGN with coverage models achieved ROUGE-1 scores 35.49, 38.19, 37.68 respectively. These neural abstractive text summarization models have also performed effectively in terms of the BLEU score and achieved BLEU-1 scores 40.60, 44.50, 45.20 respectively.
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Kumari, H., Sarkar, S., Rajput, V., Roy, A. (2020). Comparative Analysis of Neural Models for Abstractive Text Summarization. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_30
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DOI: https://doi.org/10.1007/978-981-15-6318-8_30
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