Comparing Semantic Models for Evaluating Automatic Document Summarization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926384" target="_blank" >RIV/49777513:23520/15:43926384 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-24033-6_29" target="_blank" >http://dx.doi.org/10.1007/978-3-319-24033-6_29</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-24033-6_29" target="_blank" >10.1007/978-3-319-24033-6_29</a>

Result language
angličtina
Original language name
Comparing Semantic Models for Evaluating Automatic Document Summarization
Original language description
The main focus of this paper is the examination of semantic modelling in the context of automatic document summarization and its evaluation. The main area of our research is extractive summarization, more specifically, contrastive opinion summarization.And as it is with all summarization tasks, the evaluation of their performance is a challenging problem on its own. Nowadays, the most commonly used evaluation technique is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It includes measures (such as the count of overlapping n-grams or word sequences) for automatically determining the quality of summaries by comparing them to ideal human-made summaries. However, these measures do not take into account the semantics of words and thus, for example, synonyms are not treated as equal. We explore this issue by experimenting with various language models, examining their performance in the task of computing document similarity. In particular, we chose four semantic models (LSA, LDA,
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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