We present SMMR, a scalable sentence scoring method for query-oriented update summarization. Sentences are scored thanks to a criterion combining query relevance and dissimilarity with already read documents (history). As the amount of data in history increases, non-redundancy is prioritized over query-relevance. We show that SMMR achieves promising results on the DUC 2007 update corpus.

The rapid growth of the Internet means that more information is available than ever before. Multilingual multi-document summarisation offers a way to access this information even when it is not in a language spoken by the reader by extracting the gist from related documents and translating it automatically. This paper presents an experiment in which Maximal Marginal Relevance (MMR), a well know...

It is somewhat ironic that although youth today are saturated in media, no standardized instrument exists to measure individual differences in exposure to specific media content. Therefore, we developed and validated a scale to measure both the frequency and content of adolescents’ media exposure, which measures media exposure regardless of media channel: the Content-based Media Exposure Scale ...

A Huge amount of information is present on the WWW and lot is being added to it constantly. In this context, a query specific text summarization is one of the solutions to solve this problem. In this paper we apply MMR to accomplish the task of update summary generation.

Utilization of institutional delivery has an important implication for maternal and child health in India in terms of both morbidity and mortality. The utilization rates for institutional delivery are very low in large north Indian States. Reasons for such low institutionalized delivery care are mainly the low utilization rates of Ante Natal Care services. Safe motherhood intervention Scheme or...