@inproceedings{zheng-etal-2020-sumsum,
title = "{SUMSUM}@{FNS}-2020 Shared Task",
author = "Zheng, Siyan and
Lu, Anneliese and
Cardie, Claire",
booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "COLING",
url = "https://aclanthology.org/2020.fnp-1.25",
pages = "148--152",
abstract = "This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4{\textless}sup{\textgreater}th{\textless}/sup{\textgreater}, 1{\textless}sup{\textgreater}st{\textless}/sup{\textgreater}, 2{\textless}sup{\textgreater}nd{\textless}/sup{\textgreater} and 17{\textless}sup{\textgreater}th{\textless}/sup{\textgreater} on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.",
}
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<abstract>This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4\textlesssup\textgreaterth\textless/sup\textgreater, 1\textlesssup\textgreaterst\textless/sup\textgreater, 2\textlesssup\textgreaternd\textless/sup\textgreater and 17\textlesssup\textgreaterth\textless/sup\textgreater on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.</abstract>
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%0 Conference Proceedings
%T SUMSUM@FNS-2020 Shared Task
%A Zheng, Siyan
%A Lu, Anneliese
%A Cardie, Claire
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 dec
%I COLING
%C Barcelona, Spain (Online)
%F zheng-etal-2020-sumsum
%X This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4\textlesssup\textgreaterth\textless/sup\textgreater, 1\textlesssup\textgreaterst\textless/sup\textgreater, 2\textlesssup\textgreaternd\textless/sup\textgreater and 17\textlesssup\textgreaterth\textless/sup\textgreater on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.
%U https://aclanthology.org/2020.fnp-1.25
%P 148-152
Markdown (Informal)
[SUMSUM@FNS-2020 Shared Task](https://aclanthology.org/2020.fnp-1.25) (Zheng et al., FNP 2020)
ACL
- Siyan Zheng, Anneliese Lu, and Claire Cardie. 2020. SUMSUM@FNS-2020 Shared Task. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 148–152, Barcelona, Spain (Online). COLING.