2016 |
Malviya, Shrikant; Tiwary, Uma Shanker Knowledge Based Summarization and Document Generation using Bayesian Network Journal Article Procedia Computer Science, 89 , pp. 333 - 340, 2016, ISSN: 1877-0509, (Twelfth International Conference on Communication Networks, ICCN 2016, August 19– 21, 2016, Bangalore, India Twelfth International Conference on Data Mining and Warehousing, ICDMW 2016, August 19-21, 2016, Bangalore, India Twelfth International Conference on Image and Signal Processing, ICISP 2016, August 19-21, 2016, Bangalore, India). Abstract | Links | BibTeX | Tags: Bayesian Network, Extractive Summarization, information retrieval, Multi Document Summarization (MDS), Semantic Knowledge, Text Classification. @article{MALVIYA2016333, title = {Knowledge Based Summarization and Document Generation using Bayesian Network}, author = {Shrikant Malviya and Uma Shanker Tiwary}, url = {http://www.sciencedirect.com/science/article/pii/S1877050916311450}, doi = {https://doi.org/10.1016/j.procs.2016.06.080}, issn = {1877-0509}, year = {2016}, date = {2016-01-01}, journal = {Procedia Computer Science}, volume = {89}, pages = {333 - 340}, abstract = {In this paper an approach of Semantic Knowledge Extraction (SKE), from a set of research papers, is proposed to develop a system Summarized Research Article Generator (SRAG) which would generate a summarized research article based on the query given by a user. The SRAG stores the semantic knowledge extracted from the query relevant papers in the form of a semantic tree. Semantic Tree stores all the textual units with their score in nodes organized at different levels depending on their type such as at the bottom leaf nodes keep the words with its probability, the upper level of it represent sentences with its score, next to it paragraphs, segments and so on. Scores of all the entities are calculated in bottom to up manner, first score of words are calculated, based on words sentences are ranked and similarly all the higher levels of the knowledge tree would be scored. A method of Bayesian network is used to generate a probabilistic model which would extract the relevant information from the knowledge tree to generate a summarized article. To maintain coherency, the summarized document is generated segment-wise by combining the most relevant paragraphs. Abstract of a generated summary is shown as a sample result. To show the effectiveness of the algorithm, an intrinsic evaluation strategy, degree of representativeness (DOG) is used. DOG gives on average 50% of relevance of the summary with the source. It's been observed that the proposed approach generates a comprehensive and precise papers.}, note = {Twelfth International Conference on Communication Networks, ICCN 2016, August 19{\textendash} 21, 2016, Bangalore, India Twelfth International Conference on Data Mining and Warehousing, ICDMW 2016, August 19-21, 2016, Bangalore, India Twelfth International Conference on Image and Signal Processing, ICISP 2016, August 19-21, 2016, Bangalore, India}, keywords = {Bayesian Network, Extractive Summarization, information retrieval, Multi Document Summarization (MDS), Semantic Knowledge, Text Classification.}, pubstate = {published}, tppubtype = {article} } In this paper an approach of Semantic Knowledge Extraction (SKE), from a set of research papers, is proposed to develop a system Summarized Research Article Generator (SRAG) which would generate a summarized research article based on the query given by a user. The SRAG stores the semantic knowledge extracted from the query relevant papers in the form of a semantic tree. Semantic Tree stores all the textual units with their score in nodes organized at different levels depending on their type such as at the bottom leaf nodes keep the words with its probability, the upper level of it represent sentences with its score, next to it paragraphs, segments and so on. Scores of all the entities are calculated in bottom to up manner, first score of words are calculated, based on words sentences are ranked and similarly all the higher levels of the knowledge tree would be scored. A method of Bayesian network is used to generate a probabilistic model which would extract the relevant information from the knowledge tree to generate a summarized article. To maintain coherency, the summarized document is generated segment-wise by combining the most relevant paragraphs. Abstract of a generated summary is shown as a sample result. To show the effectiveness of the algorithm, an intrinsic evaluation strategy, degree of representativeness (DOG) is used. DOG gives on average 50% of relevance of the summary with the source. It's been observed that the proposed approach generates a comprehensive and precise papers. |
2007 |
Siddiqui, Tanveer J; Tiwary, Uma Shanker Query Based Summary for Assessing Document Relevance Inproceedings 2006 1st International Conference on Digital Information Management, pp. 314-319, 2007. Links | BibTeX | Tags: conceptual graph, Context, Data mining, Document handling, document relevance, Explosions, graph theory, information retrieval, Information technology, Physics, query based document summarization, query processing, relevance feedback, text analysis @inproceedings{4221908, title = {Query Based Summary for Assessing Document Relevance}, author = {Tanveer J. Siddiqui and Uma Shanker Tiwary}, doi = {10.1109/ICDIM.2007.369216}, year = {2007}, date = {2007-12-01}, booktitle = {2006 1st International Conference on Digital Information Management}, pages = {314-319}, keywords = {conceptual graph, Context, Data mining, Document handling, document relevance, Explosions, graph theory, information retrieval, Information technology, Physics, query based document summarization, query processing, relevance feedback, text analysis}, pubstate = {published}, tppubtype = {inproceedings} } |
Publications
2016 |
Knowledge Based Summarization and Document Generation using Bayesian Network Journal Article Procedia Computer Science, 89 , pp. 333 - 340, 2016, ISSN: 1877-0509, (Twelfth International Conference on Communication Networks, ICCN 2016, August 19– 21, 2016, Bangalore, India Twelfth International Conference on Data Mining and Warehousing, ICDMW 2016, August 19-21, 2016, Bangalore, India Twelfth International Conference on Image and Signal Processing, ICISP 2016, August 19-21, 2016, Bangalore, India). |
2007 |
Query Based Summary for Assessing Document Relevance Inproceedings 2006 1st International Conference on Digital Information Management, pp. 314-319, 2007. |