Web Document Summarization by Context This paper adresses the issue of Web document summarization. We consider the context of a Web document by the set of pieces of information extracted from the content of all the documents linked to it. We put forward two new summarization by context algorithms. The first one uses both the content and the context and the second one relies only on the elements of the context. It is shown that summaries based on the context are usually much more relevant than those only made from the content of the target. Optimal conditions on the size of the content and the context of the document to yield the best summaries are studied.

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.