Annotation of Scientific Summaries for Information Retrieval.

Fidelia Ibekwe-Sanjuan 1, * Fernandez Silvia 2 Sanjuan Eric 2 Charton Eric 2
* Corresponding author
1 ELICO Lyon 3
ELICO - Equipe de recherche de Lyon en sciences de l'information et de la communication
Abstract : We present a methodology combining surface NLP and Machine Learning techniques for ranking asbtracts and generating summaries based on annotated corpora. The corpora were annotated with meta-semantic tags indicating the category of information a sentence is bearing (objective, findings, newthing, hypothesis, conclusion, future work, related work). The annotated corpus is fed into an automatic summarizer for query-oriented abstract ranking and multi- abstract summarization. To adapt the summarizer to these two tasks, two novel weighting functions were devised in order to take into account the distribution of the tags in the corpus. Results, although still preliminary, are encouraging us to pursue this line of work and find better ways of building IR systems that can take into account semantic annotations in a corpus.
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Conference papers
Omar Alonso ; Hugo Zaragoza. ECIR'08 Workshop on: Exploiting Semantic Annotations for Information Retrieval, Mar 2008, Glasgow, United Kingdom. pp.70-83


https://hal.archives-ouvertes.fr/hal-00635699
Contributor : Fidelia Ibekwe-SanJuan <>
Submitted on : Tuesday, October 25, 2011 - 5:14:40 PM
Last modification on : Tuesday, February 3, 2015 - 4:04:49 PM

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  • ARXIV : 1110.5722

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Fidelia Ibekwe-Sanjuan, Fernandez Silvia, Sanjuan Eric, Charton Eric. Annotation of Scientific Summaries for Information Retrieval.. Omar Alonso ; Hugo Zaragoza. ECIR'08 Workshop on: Exploiting Semantic Annotations for Information Retrieval, Mar 2008, Glasgow, United Kingdom. pp.70-83. <hal-00635699>

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