An Automatic Process to Convert Documents into Abstracts by Using Natural Language Processing Techniques
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.
KeywordsInformation overload TFxIDF Natural Language Processing Techniques Summarization
Unable to display preview. Download preview PDF.
- 1.Tanasa, D.: Advanced Data Preprocessing Mining. IEEE Intelligent Systems 19(2)Google Scholar
- 3.Buckley, C., Cardie, C.: SMART Summarization System. In: Hand, T.F., Sundheim, B, eds. (1997)Google Scholar
- 4.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)Google Scholar
- 5.Dhillon, I.S.: A divisive information theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research 3 (2003)Google Scholar
- 6.Mihalcea, R., Tarau, P.: A Language Independent Algorithm for Single and Multiple Document Summarization. University of North TexasGoogle Scholar
- 7.Alam, H., Kumar, A., Nakamura, M.: Structured and Unstructured Document Summarization: Design of a Commercial Summarizer using Lexical ChainsGoogle Scholar
- 8.Santorini, B.: Part-of-Speech Tagging Guidelines for the Penn Treebank ProjectGoogle Scholar
- 9.World of computing.net/pos-tagging/markov-models.htmlGoogle Scholar
- 10.A survey of named entity recognition and classification David Nadeau. Satoshi Sekine National Research Council Canada / New York UniversityGoogle Scholar
- 11.dictionary generation for low-resourced language pairs Varga István Yamagata uNIVERSITY, Graduate School of Science and Engineering email@example.comGoogle Scholar
- 12.Ontology’s, Web 2.0 and Beyond. Keynote presentation at the Ontology Summit 2007 – Ontology, Taxonomy, Folksonomy: Understanding theDistinctions (March 1, 2007)Google Scholar
- 13.Detecting Opinions Using Deep Syntactic Analysis Caroline Brun Xerox Research Centre Europe Meylan, FranceGoogle Scholar