A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Study of Chinese Text Summarization Using Adaptive Clustering of Paragraphs
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
The automatic creation of literature abstracts
IBM Journal of Research and Development
Extractive summarization based on word information and sentence position
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
An improved approach to extract document summaries based on popularity
DNIS'05 Proceedings of the 4th international conference on Databases in Networked Information Systems
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In this paper, we propose a new method for text summarization. The system finds topic word and event word firstly, and then recalculates word weight. Using recalculated word weight to compute similarly of paragraphs to search local topics units. The most representative sentences in each local topic unit are selected as the summary sentences. By analyzing semantic structure of the documents first, the summary sentences are not redundancy and the coverage of each local topic is balanced Experimental results show that our approach is effective and efficient, and performance of the system is reliable.