Abstract
The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a) positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.
Cite
CITATION STYLE
Lloret, E., Boldrini, E., Martínez-Barco, P., & Palomar, M. (2017). Ultra-Concise Multi-genre Summarisation of Web2.0: towards Intelligent Content Generation. In MultiLing 2017 - Workshop on Summarization and Summary Evaluation Across Source Types and Genres, Proceedings of the Workshop (pp. 37–46). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-1006
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