A System for Summarizing Scientific Topics Starting from Keywords
In this paper, we investigate the problem of automatic generation of scientific surveys starting from keywords provided by a user. We present a system that can take a topic query as input and generate a survey of the topic by first selecting a set of relevant documents, and then selecting relevant sentences from those documents. We discuss the issues of robust evaluation of such systems and describe an evaluation corpus we generated by manually extracting factoids, or information units, from 47 gold standard documents (surveys and tutorials) on seven topics in Natural Language Processing. We have manually annotated 2,625 sentences with these factoids (around 375 sentences per topic) to build an evaluation corpus for this task. We present evaluation results for the performance of our system using this annotated data.