Akshi Kumar
Anand Nayyar
ABSTRACT
In the contemporary age of information, accessing data becomes easy, but finding knowledge is very difficult. The participation & publishing of information has consequently escalated the suffering of 'Information Glut.' Assisting users' informational searches with reduced reading or surfing time by extracting and evaluating accurate, authentic & relevant information are the primary concerns in the present milieu. Automatic text summarization condenses an original document into a shorter form to create a smaller, compact version from the abundant information that is available, preserving the content & meaning such that it meets the needs of the user. Though many summarization techniques have been proposed, there are no 'silver bullets' to achieve the superlative results as of human-generated summaries. Fuzzy Logic has appeared as a robust theoretical framework for studying human reasoning. A new hybrid model based on fuzzy logic has been proposed using two graph-based techniques named TextRank and LexRank and one semantic-based technique named Latent semantic analysis (LSA). The techniques are evaluated on the Opinosis dataset using 'ROUGE-1' (Recall-Oriented Understudy for Gisting Evaluation-1) and 'time to extract the keywords.' The proposed technique has outperformed the existing techniques when compared with the results given by the original studies.
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Index Terms
- Fuzzy Logic based Hybrid Model for Automatic Extractive Text Summarization
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