Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Information Retrieval
Machine Learning
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Computational Linguistics - Summarization
A Biologically Inspired Connectionist System for Natural Language Processing
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
A Connectionist Thematic Grid Predictor for Pre-parsed Natural Language Sentences
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
GistSumm: a summarization tool based on a new extractive method
PROPOR'03 Proceedings of the 6th international conference on Computational processing of the Portuguese language
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
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An implementation of a computational tool to generate new summaries from new source texts is presented, by means of the connectionist approach (artificial neural networks). Among other contributions that this work intends to bring to natural language processing research, the use of a more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expectation that it may bring an increase in computational efficiency when compared to the so-called biologically implausible algorithms.