TY - GEN
T1 - SCE-SUMMARY at the FNS 2020 shared task
AU - Litvak, Marina
AU - Vanetik, Natalia
AU - Puchinsky, Tzvi
N1 - Publisher Copyright:
© 2020 FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings. All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.
AB - With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.
UR - http://www.scopus.com/inward/record.url?scp=85123315981&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85123315981
T3 - FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings
SP - 124
EP - 129
BT - FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings
A2 - El-Haj, Mahmoud
A2 - Athanasakou, Vasiliki
A2 - Ferradans, Sira
A2 - Salzedo, Catherine
A2 - Elhag, Ans
A2 - Bouamor, Houda
A2 - Litvak, Marina
A2 - Rayson, Paul
A2 - Giannakopoulos, George
A2 - Pittaras, Nikiforos
PB - Association for Computational Linguistics (ACL)
T2 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, FNP-FNS 2020
Y2 - 12 December 2020
ER -