Temporal TF-IDF: a high performance approach for event summarization in Twitter

Alsaedi, Nasser, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 2017. Temporal TF-IDF: a high performance approach for event summarization in Twitter. Presented at: IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 13-16 October 2016. 2016 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE, pp. 515-521. 10.1109/WI.2016.0087

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Abstract

In recent years, there has been increased interest in real-world event summarization using publicly accessible data made available through social networking services such as Twitter and Facebook. People use these outlets to communicate with others, express their opinion and commentate on a wide variety of real-world events. Due to the heterogeneity, the sheer volume of text and the fact that some messages are more informative than others, automatic summarization is a very challenging task. This paper presents three techniques for summarizing microblog documents by selecting the most representative posts for real-world events (clusters). In particular, we tackle the task of multilingual summarization in Twitter. We evaluate the generated summaries by comparing them to both human produced summaries and to the summarization results of similar leading summarization systems. Our results show that our proposed Temporal TF-IDF method outperforms all the other summarization systems for both the English and non-English corpora as they lead to informative summaries.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	978-1-5090-4470-2
Last Modified:	20 Nov 2022 07:24
URI:	https://orca.cardiff.ac.uk/id/eprint/97626

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