Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A statistical approach to machine translation
Computational Linguistics
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Generating concise natural language summaries
Information Processing and Management: an International Journal - Special issue: summarizing text
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Selecting text spans for document summaries: heuristics and metrics
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Automatic title generation for EM
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Information Retrieval
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
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In this paper, we present and compare automatically generated titles for machine-translated documents using several different statistics-based methods. A Naïve Bayesian, a K-Nearest Neighbour, a TF-IDF and an iterative Expectation-Maximization method for title generation were applied to 1000 original English news documents and again to the same documents translated from English into Portuguese, French or German and back to English using SYSTRAN. The AutoSummarization function of Microsoft Word was used as a base line. Results on several metrics show that the statistics-based methods of title generation for machine-translated documents are fairly language independent and title generation is possible at a level approaching the accuracy of titles generated for the original English documents.