TY - CHAP
T1 - A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
AU - Wang, Xinyu
AU - Gui, Lin
AU - He, Yulan
N1 - Publisher Copyright:
©2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner. In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports from 563 companies spanning from 2001 to 2022. These reports pose significant challenges due to their diverse structures and extensive length. To address these challenges, we propose a new framework for Toc extraction, consisting of three steps: (1) Constructing an initial tree of text blocks based on reading order and font sizes; (2) Modelling each tree node (or text block) independently by considering its contextual information captured in node-centric subtree; (3) Modifying the original tree by taking appropriate action on each tree node (Keep, Delete, or Move). This construction-modelling-modification (CMM) process offers several benefits. It eliminates the need for pairwise modelling of section headings as in previous approaches, making document segmentation practically feasible. By incorporating structured information, each section heading can leverage both local and long-distance context relevant to itself. Experimental results show that our approach outperforms the previous state-of-the-art baseline with a fraction of running time. Our framework proves its scalability by effectively handling documents of any length.
AB - Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner. In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports from 563 companies spanning from 2001 to 2022. These reports pose significant challenges due to their diverse structures and extensive length. To address these challenges, we propose a new framework for Toc extraction, consisting of three steps: (1) Constructing an initial tree of text blocks based on reading order and font sizes; (2) Modelling each tree node (or text block) independently by considering its contextual information captured in node-centric subtree; (3) Modifying the original tree by taking appropriate action on each tree node (Keep, Delete, or Move). This construction-modelling-modification (CMM) process offers several benefits. It eliminates the need for pairwise modelling of section headings as in previous approaches, making document segmentation practically feasible. By incorporating structured information, each section heading can leverage both local and long-distance context relevant to itself. Experimental results show that our approach outperforms the previous state-of-the-art baseline with a fraction of running time. Our framework proves its scalability by effectively handling documents of any length.
UR - http://www.scopus.com/inward/record.url?scp=85184803182&partnerID=8YFLogxK
M3 - Conference paper
AN - SCOPUS:85184803182
T3 - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 13215
EP - 13229
BT - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
A2 - Bouamor, Houda
A2 - Pino, Juan
A2 - Bali, Kalika
PB - Association for Computational Linguistics (ACL)
T2 - 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Y2 - 6 December 2023 through 10 December 2023
ER -