A Framework for Word Embedding Based Automatic Text Summarization and Evaluation Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like RecallOriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping ngram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient topn sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.

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.