Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
Headline generation based on statistical translation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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The important mass of textual documents is in perpetual growth and requires strong applications to automatically process information. Automatic titling is an essential task for several applications: 'No Subject' e-mails titling, text generation, summarization, and so forth. This study presents an original approach consisting in titling journalistic articles by nominalizing. In particular, morphological and semantic processing are employed to obtain a nominalized form which has to respect titles characteristics (in particular, relevance and catchiness). The evaluation of the approach, described in the paper, indicates that titles stemming from this method are informative and/or catchy.