Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
<|reference_start|>Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies: We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.<|reference_end|>
@article{radev2000centroid-based,
    title={Centroid-based summarization of multiple documents: sentence extraction,
  utility-based evaluation, and user studies},
    author={Dragomir R. Radev (University of Michigan), Hongyan Jing (Columbia
  University), Malgorzata Budzikowska (IBM TJ Watson Research Center)},
    journal={NAACL/ANLP Workshop on Automatic Summarization, Seattle, WA, April
  30, 2000},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0005020},
    primaryClass={cs.CL cs.AI cs.DL cs.HC cs.IR}
}