  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.
