Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter

Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter

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, such as disasters and public disorder. 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.

Added By:	Ms Amber Bu
Date Added:	16 Feb 2017 11:13
Creators Name:	Pete Burnap
Tags:	web science, wais seminar, WAIS seminar, Web Science, WAIS Research Seminar, computer science, Research Seminar, Wais Research Seminar, ECS, WAIS, WAIS Semiar, Event Summarization, Social Data
Viewing permissions:	University
Link:	http://edshare.soton.ac.uk/id/eprint/18276
Downloads & Views

Actions (login required)

View Item