CRF-based Arabic Opinion Summarization System
نویسندگان
چکیده
This paper presents the study that we have carried out to investigate supervised opinion summarization in Modern Standard Arabic. We use a corpus of news articles. We use conditional random fields (CRF) as machine learning technique. We investigate some features to identify those that allow achieving the best results. Our contribution is to use opinion specific features to summarize Arabic news articles using CRF models. Experimental results show that our proposed approach is very effective for assigning features to sentences.
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