A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Generating summaries of multiple news articles
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
An automatic extraction of key paragraphs based on context dependency
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
The TIPSTER SUMMAC Text Summarization Evaluation
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Fast generation of abstracts from general domain text corpora by extracting relevant sentences
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Hi-index | 0.00 |
This paper proposes a method for extracting key paragraph for multi-document summarization based on distinction between a topic and an event. A topic and an event are identified using a simple criterion called domain dependency of words. The method was tested on the TDT1 corpus which has been developed by the TDT Pilot Study and the result can be regarded as promising the idea of domain dependency of words effectively employed.