Intweetive Text Summarization The amount of user generated contents from various social medias allows analyst to handle a wide view of conversations on several topics related to their business. Nevertheless keeping uptodate with this amount of information is not humanly feasible. Automatic Summarization then provides an interesting mean to digest the dynamics and the mass volume of contents. In this paper, we address the issue of tweets summarization which remains scarcely explored. We propose to automatically generated summaries of MicroBlogs conversations dealing with public figures EReputation. These summaries are generated using keyword queries or sample tweet and offer a focused view of the whole MicroBlog network. Since stateoftheart is lacking on this point we conduct and evaluate our experiments over the multilingual CLEF RepLab TopicDetection dataset according to an experimental evaluation process.

Summarizing Usergenerated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries. As the amount of usergenerated textual content grows rapidly, text summarization algorithms are increasingly being used to provide users a quick overview of the information content. Traditionally, summarization algorithms have been evaluated only based on how well they match humanwritten summaries (e.g. as measured by ROUGE scores). In this work, we propose to evaluate summarization algorithms from a completely new perspective that is important when the usergenerated data to be summarized comes from different socially salient user groups, e.g. men or women, Caucasians or AfricanAmericans, or different political groups (Republicans or Democrats). In such cases, we check whether the generated summaries fairly represent these different social groups. Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e.g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not. Our experiments over realworld microblog datasets show that existing summarization algorithms often represent the socially salient usergroups very differently compared to their distributions in the original data. More importantly, some groups are frequently underrepresented in the generated summaries, and hence get far less exposure than what they would have obtained in the original data. To reduce such adverse impacts, we propose novel fairnesspreserving summarization algorithms which produce highquality summaries while ensuring fairness among various groups. To our knowledge, this is the first attempt to produce fair text summarization, and is likely to open up an interesting research direction.