@inproceedings{premi-etal-2020-amex,
title = "{AMEX}-{AI}-{LABS}: Investigating Transfer Learning for Title Detection in Table of Contents Generation",
author = "Premi, Dhruv and
Badugu, Amogh and
Sharad Bhatt, Himanshu",
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-1.26",
pages = "153--157",
abstract = "We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.",
}
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<abstract>We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.</abstract>
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%0 Conference Proceedings
%T AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation
%A Premi, Dhruv
%A Badugu, Amogh
%A Sharad Bhatt, Himanshu
%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 premi-etal-2020-amex
%X We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.
%U https://aclanthology.org/2020.fnp-1.26
%P 153-157
Markdown (Informal)
[AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation](https://aclanthology.org/2020.fnp-1.26) (Premi et al., FNP 2020)
ACL