Applying Natural Language Generation to Indicative Summarization
<|reference_start|>Applying Natural Language Generation to Indicative Summarization: The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries.<|reference_end|>
@article{kan2001applying,
    title={Applying Natural Language Generation to Indicative Summarization},
    author={Min-Yen Kan, Kathleen R. McKeown, Judith L. Klavans},
    journal={arXiv preprint arXiv:cs/0107019},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0107019},
    primaryClass={cs.CL}
}