TY - GEN
T1 - Automated pyramid summarization evaluation
AU - Gao, Yanjun
AU - Sun, Chen
AU - Passonneau, Rebecca J.
N1 - Publisher Copyright:
© 2019 Association for Computational Linguistics.
PY - 2019
Y1 - 2019
N2 - Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.
AB - Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.
UR - https://www.scopus.com/pages/publications/85084339079
UR - https://www.scopus.com/pages/publications/85084339079#tab=citedBy
U2 - 10.18653/v1/K19-1038
DO - 10.18653/v1/K19-1038
M3 - Conference contribution
AN - SCOPUS:85084339079
T3 - CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
SP - 404
EP - 418
BT - CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
PB - Association for Computational Linguistics
T2 - 23rd Conference on Computational Natural Language Learning, CoNLL 2019
Y2 - 3 November 2019 through 4 November 2019
ER -