@inproceedings{ait-azzi-kang-2020-extractive,
title = "Extractive Summarization System for Annual Reports",
author = "Ait Azzi, Abderrahim and
Kang, Juyeon",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
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-1.24/",
pages = "143--147",
abstract = "In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an {\textquotedblleft}extractive{\textquotedblright} summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles."
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<abstract>In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an “extractive” summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles.</abstract>
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%0 Conference Proceedings
%T Extractive Summarization System for Annual Reports
%A Ait Azzi, Abderrahim
%A Kang, Juyeon
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F ait-azzi-kang-2020-extractive
%X In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an “extractive” summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles.
%U https://aclanthology.org/2020.fnp-1.24/
%P 143-147
Markdown (Informal)
[Extractive Summarization System for Annual Reports](https://aclanthology.org/2020.fnp-1.24/) (Ait Azzi & Kang, FNP 2020)
ACL
- Abderrahim Ait Azzi and Juyeon Kang. 2020. Extractive Summarization System for Annual Reports. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 143–147, Barcelona, Spain (Online). COLING.