Abstractive microblogs summarization
Nataliia Uvarova · BIBSYS Brage (BIBSYS (Norway)) · 2015
Microblogging is a new electronic communication medium based on short status updates containing personal and instant information. Due to the popularity of microblogs, the volume of information is enormous and big portion of it is duplicative or irrelevant. The effective way to summarize information can be used by scientists, journalists and marketing analysts to get cleverer insights about people’s reactions and opinions on different topics: political debates, sport events or product presentations. Existing summarization algorithms can be enhanced in several ways. The first way is to add sentiment analysis. As information in microblogs is very opinionated, analyzing tweets polarity can improve machine summaries by selecting more sentiment tweets than pure topical. Another enhancement is to use different summary length for different topics. Previous studies often limit summaries to be particular length. Relaxing this restriction can present summaries that are more optimal for a particular topic. The goal of this research is to perform qualitative study of these enhancements and to provide insights and suggestions for conducting bigger qualitative research. In total ten topics are selected, for which human summaries are compared to state-of-the-art non-sentiment and sentiment summarizers. Resulting observations are the following: there is more topical than sentiment content in summaries generated by humans, however individual biases could be against the trend; the length of the summary is an important feature that influences both generation of human summaries and interpretation of evaluation results, different topics require summaries of different length; sentiment summarization doesn’t produce better results for any evaluation metric used, but there could be possibility for its application in proper settings with specific topics.