Open AccessA Pointer Generator Network Model to Automatic Text Summarization and Headline Generation
Author(s) -
Anubha Agrawal,
Sakshi Saraswat,
Hira Javed
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1094.0785s319
Subject(s) - automatic summarization , headline , pointer (user interface) , computer science , encoder , generator (circuit theory) , text generation , artificial intelligence , artificial neural network , recurrent neural network , natural language processing , philosophy , linguistics , power (physics) , physics , quantum mechanics , operating system
In a world where information is growing rapidly every single day, we need tools to generate summary and headlines from text which is accurate as well as short and precise. In this paper, we have described a method for generating headlines from article. This is done by using hybrid pointer-generator network with attention distribution and coverage mechanism on article which generates abstractive summarization followed by the application of encoder-decoder recurrent neural network with LSTM unit to generate headlines from the summary. Hybrid pointer generator model helps in removing inaccuracy as well as repetitions. We have used CNN / Daily Mail as our dataset.