Abstract
Now a days each and every people using internet and collects the information. At the same time the internet is growing exponentially, huge amount of information is available online. That’s why the information overload problem is faced by every end user. So Automatic Process of Document Abstracts is recognized as an important task. For this intention we used various approaches these are Anaphora resolution, mining methods and TFxIDF. However these techniques have some limitations and mainly the drawback is from the end user’s perspective, the requestor may not be aware of all the knowledge that constitutes the methods. That’s why in this paper we focussed on developing Abstracts, that is Summarization method based on Natural Language Processing Techniques. At the same time it is also useful to multi-documents summarization. We explore some of the metrics and evaluation strategies, features in document abstracts or summarization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tanasa, D.: Advanced Data Preprocessing Mining. IEEE Intelligent Systems 19(2)
Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2(2), 159–165 (1958)
Buckley, C., Cardie, C.: SMART Summarization System. In: Hand, T.F., Sundheim, B, eds. (1997)
Radev, D.R., Jing, H., Budzikowska, M.: Summarization of multiple documents: clustering, sentence extraction, and evaluation. In: ANLP/NAACL Workshop on Summarization, Seattle, WA (April 2000)
Dhillon, I.S.: A divisive information theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research 3 (2003)
Mihalcea, R., Tarau, P.: A Language Independent Algorithm for Single and Multiple Document Summarization. University of North Texas
Alam, H., Kumar, A., Nakamura, M.: Structured and Unstructured Document Summarization: Design of a Commercial Summarizer using Lexical Chains
Santorini, B.: Part-of-Speech Tagging Guidelines for the Penn Treebank Project
World of computing.net/pos-tagging/markov-models.html
A survey of named entity recognition and classification David Nadeau. Satoshi Sekine National Research Council Canada / New York University
dictionary generation for low-resourced language pairs Varga István Yamagata uNIVERSITY, Graduate School of Science and Engineering dyn36150@dip.yz.yamagata-u.ac.jp
Ontology’s, Web 2.0 and Beyond. Keynote presentation at the Ontology Summit 2007 – Ontology, Taxonomy, Folksonomy: Understanding theDistinctions (March 1, 2007)
Detecting Opinions Using Deep Syntactic Analysis Caroline Brun Xerox Research Centre Europe Meylan, France
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Jayaraju, C., Basha, Z.N., Madhavarao, E., Kalyani, M. (2014). An Automatic Process to Convert Documents into Abstracts by Using Natural Language Processing Techniques. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-03107-1_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03106-4
Online ISBN: 978-3-319-03107-1
eBook Packages: EngineeringEngineering (R0)