A hybrid approach for automatic document summarization

Publication Type:

Conference Paper

Authors:

Siji Rani S.; Sreejith, K; Sanker, Arjun

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

URL:

https://ieeexplore.ieee.org/abstract/document/8125917

Keywords:

automatic document summarization, automatic text summarization, Data preprocessing, document context, extractive approach, Graph theory, Hybrid approach, Information Retrieval, learning (artificial intelligence), Machine learning, NAtural language processing, natural language processing tools, Probabilistic logic, scoring system, sentence similarity, Silicon, single document summarization, Speech, text analysis, textual information, Tools, weighted undirected graph

Abstract:

Automatic text summarization come under the domains of natural language processing, machine learning and information retrieval. As the abundance of textual information grows so does the need for summarising it. Here we consider a method for single document summarization using natural language processing tools and an extractive approach based on sentence similarity and document context. The technique uses a weighted undirected graph based scoring on paragraphs and a word frequency based scoring system on the entire document to obtain summaries. The proposed method is validated through experiments and the results are promising.

Cite this Research Publication

Siji Rani S., Sreejith, K., and Sanker, A., “A hybrid approach for automatic document summarization”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.

 
          
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