Automatic Text Summarization
Keywords:
Text summarization, Extractive summary, abstractive summary, data extraction, novel sentences etc.Abstract
The amount of data available to us both online and offline is growing extremely and due to this summarization became increasingly important
over the past few years. Automatic Summarization is the process of reducing a document to a small document called summary that contains
the significant or key points of the original document. Automatic Text Summarization is broadly classified into two categories i.e Extractive Summarization and Abstractive Summarization. Extractive Summary is a subset of the original document that represents important data contained in the document. An abstractive summary may contain some novel sentences that are not present in the whole document. In this context we are working on extractive summarization where we are including important sentences from the document in the summary with limited length. Automatic summarization is a technique by which the data is extracted and shorten from text document by the use of software .
References
Sajjan RS, Shinde MG. A detailed survey On automatic text summarization. VVPIET Solapur University 16/06/2019.
Sahoo A, Nayak AK. Review paper on extractive text summarization. S’O’A University, Bhubaneshwar 04/04/2018.
Gaikwad DK. A Review paper on text summarization. Dr. BAMU, Aurangabad 03/03/2016.
Galgani F, Compton P, Hoffmann A. Combining different summarization techniques for legal text. In Proceedings
of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, Association for Computational Linguistics, 2012; 115–123.
Mihalcea R. Graph-based ranking algorithms for sentence extraction, ap- plied to text summarization. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, page 20. Association for Computational Lin- guistics, 2004.
