Abstract
Nowadays, the efficient access of enormous amounts of information has become more difficult due to the rapid growth of the Internet. To manage the vast information, we need efficient and effective methods and tools. In this paper, a graph-based text summarization method has been described which captures the aboutness of a text document. The method has been developed using modified TextRank computed based on the concept of PageRank defined for each page in the Web pages. The proposed method constructs a graph with sentences as the nodes and similarity between two sentences as the weight of the edge between them. Modified inverse sentence frequency-cosine similarity is used to give different weightage to different words in the sentence, whereas traditional cosine similarity treats the words equally. The graph is made sparse and partitioned into different clusters with the assumption that the sentences within a cluster are similar to each other and sentences of different cluster represent their dissimilarity. The performance evaluation of proposed summarization technique shows the effectiveness of the method.
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Mallick, C., Das, A.K., Dutta, M., Das, A.K., Sarkar, A. (2019). Graph-Based Text Summarization Using Modified TextRank. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_14
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DOI: https://doi.org/10.1007/978-981-13-0514-6_14
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