A new approach to unsupervised text summarization

SIGIR Forum (ACM Special Interest Group on Information Retrieval)

Volumn , Issue , 2001, Pages 26-34

  Nomoto, Tadashi ^a   Matsumoto, Yuji ^b  

^a National Institutes for the Humanities   (Japan)

^b NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

BMIR J2; Clustering; MDL; Text Summarization

Indexed keywords

COMPUTATIONAL LINGUISTICS; DATABASE SYSTEMS; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; LEARNING SYSTEMS;

TEXT SUMMARIZATION;

TEXT PROCESSING;

EID: 0034785303     PISSN: 01635840     EISSN: None     Source Type: Journal    
DOI: 10.1145/383952.383956     Document Type: Conference Paper

References (23)

1. Query-relevant summarization using faqs
2. Refining initial points for k-means clustering
3. The use of MMR, diversity-based reranking for reordering documents and producing summaries
4. Text summarization by sentence segment extraction using machine learning algorithms
5. New method in automatic abstracting
6. Summarization evaluation methods: Experiments and analysis
7. Distribution of content words and phrases in text and language modelling
8. A trainable document summarizer
9. The automatic creation of literary abstracts
10. The rhetorical parsing of natural language texts
11. The automated construction of large-scale corpora for summarization research
12. The effects and limitations of automated text condensing on reading comprehension performance
13. Data reliability and its effects on automatic abstracting
14. X-means: Extending K-means with efficient estimation of the number of clusters
15. Centroid-based summarization of multiple documents: Sentence extraction, utility-based evaluation, and user studies
16. Stochastic complexity in learning
17. Automatic text structuring and summarization
18. Fast generation of abstracts from general domain text corpora by extracting relevant sentences