Manual and automatic evaluation of summaries
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Summary in context: Searching versus browsing
ACM Transactions on Information Systems (TOIS)
CollabSum: exploiting multiple document clustering for collaborative single document summarizations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic keyphrase extraction from scientific documents using N-gram filtration technique
Proceedings of the eighth ACM symposium on Document engineering
Multi-sentence compression: finding shortest paths in word graphs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
SemanticRank: ranking keywords and sentences using semantic graphs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Towards a unified approach to simultaneous single-document and multi-document summarizations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automated extractive single-document summarization: beating the baselines with a new approach
Proceedings of the 2011 ACM Symposium on Applied Computing
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Combining syntax and semantics for automatic extractive single-document summarization
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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Summarization mainly provides the major topics or theme of document in limited number of words. However, in extract summary we depend upon extracted sentences, while in abstract summary, each summary sentence may contain concise information from multiple sentences. The major facts which affect the quality of summary are: (1) the way of handling noisy or less important terms in document, (2) utilizing information content of terms in document (as, each term may have different levels of importance in document) and (3) finally, the way to identify the appropriate thematic facts in the form of summary. To reduce the effect of noisy terms and to utilize the information content of terms in the document, we introduce the graph theoretical model populated with semantic and statistical importance of terms. Next, we introduce the concept of weighted minimum vertex cover which helps us in identifying the most representative and thematic facts in the document. Additionally, to generate abstract summary, we introduce the use of vertex constrained shortest path based technique, which uses minimum vertex cover related information as valuable resource. Our experimental results on DUC-2001 and DUC-2002 dataset show that our devised system performs better than baseline systems.