Daniel@ F in TOC ’2 Shared Task: Title Detection and Structure Extraction (2020)

Abstract: We present our contributions for the 2020 FinTOC Shared Tasks: Title Detection and Table of Contents Extraction. For the Structure Extraction task, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge. For the title detection task, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.

## introduction the fintoc'2 financial document structure extraction competition @xcite proposed to evaluate two tasks : title detection and toc structure extraction. structure extraction is an important issue for natural language processing and document analysis. rich logical structures can be exploited for document classification and clustering @xcite . in the document analysis field, toc generation aims to retrieve or extract a toc from documents where the logical structure is not explicitly marked. toc makes it easier to access information, in particular in digital humanities where documents can be long and structured in parts, chapters, appendices. title detection plays an important role for extracting the structure by helping to get candidates to populate the toc. furthermore, the position of sentences with respect to titles is used to improve the results in some nlp tasks: text classification @xcite , terminology acquisition @xcite or keyphrase extraction @xcite . the paper is organized as follows. section 2 gives a quick background for both subtasks. section 3 describes our contribution to toc extraction and section 4 our contribution to title detection. we give some words of conclusion in section 5. ## background usually, the logical structure of natural language data is not explicitly encoded within a pdf document, it is the case for most financial prospectuses. the organization of the information has to be inferred from the layout and the style of text blocks. positional and contrastive features allows the recovery of the underlying structure. recovering the global structure of a document is an important process to achieve for information extraction. in this regard, document structure analysis certainly precedes sentence analysis. by the way, neither one of them can be seen as preprocessing stage. both are fully part of a natural language processing system. these processes relate to skimming and scanning reading techniques. while skimming allows a reader to get a first glance of a document, scanning is the process of searching for a specific piece of information. different parts of the document may be spotted by the reader and sought for specific information using a zoom-in/zoom-out strategy @xcite . concerning global structure, important information is found in the titles and subtitles, making the detection of titles important for improving web indexation @xcite or downstream nlp tasks @xcite . we can see two main strategies for toc extraction: detecting the toc pages and relying on the book content. the icdar book structure extraction competitions results @xcite showed that hybrid systems are promising which is consistent with more recent results from @xcite who combined different systems to get better results. ## conclusion in this article we proposed approaches for two shared tasks of fintoc 2020. regarding the toc extraction shared task, we propose a hybrid approach. it consists in combining the output of multiple modules dedicated or related to title detection. title candidates are extracted from the table of contents thanks to a toc detection and extraction module. candidates are also extracted from the main text stream with the help of two complementary modules: a numbered list detection module and a text saliency detection module. in order to enrich the approach, titles from the training set are used to detect domain-specific titles. the title candidates are merged to generate a complete table of contents. for the title detection task, we proposed to use two types of features: surface features and character n-grams. we showed that stylometric features (frequency of punctuation, numbers and capitalized letters) combined with visual characteristics (bold, italic. . . ) achieve better results than the character n-gram approaches.