Social context summarization using user-generated content and third-party sources

In the context of social media, users mutually share their interests of an event mentioned in a Web document. Its content can also be found in different news providers with a writing variation. This paper presents a framework which exploits the support of social context (user-generated content such as comments or tweets and third-party sources such as relevant documents retrieved from a search engine) to extract high-quality summaries. The extraction was formulated in two steps: sentence scoring and selection. The scoring is modeled as a learning to rank problem, which employs Ranking SVM to mutually exploits sentences, user-generated content, and third-party sources in the form of features to cover summary aspects. For the selection, summaries are extracted by using a score-based or voting method. For evaluation, three datasets of sentence and highlight extraction in two languages were taken as a case study. Experimental results indicate that by integrating user-generated content and third-party sources, our framework obtains improvements of ROUGE-scores over state-of-the-art methods for single-document summarization.