Hierarchical Text Segmentation for Medieval Manuscripts (2020)

Abstract: In this paper, we address the segmentation of books of hours, Latin devotional manuscripts of the late Middle Ages, that exhibit challenging issues: a complex hierarchical entangled structure, variable content, noisy transcriptions with no sentence markers, and strong correlations between sections for which topical information is no longer sufficient to draw segmentation boundaries. We show that the main state-of-the-art segmentation methods are either inefficient or inapplicable for books of hours and propose a bottom-up greedy approach that considerably enhances the segmentation results. We stress the importance of such hierarchical segmentation of books of hours for historians to explore their overarching differences underlying conception about Church.

## introduction text segmentation is essential in many downstream applications including document understanding and navigation, summarization, information retrieval and discourse parsing @xcite @xcite . traditional unsupervised approaches assume a high correlation between segments and subtopics. therefore, based on a prior text decomposition, two adjacent segments are merged if they are highly correlated. on the contrary, if their similarity is below a certain threshold, a shift is determined @xcite . when sufficient topically annotated training data are available, deep neural approaches based on cnn @xcite or lstm @xcite can be efficiently applied @xcite . until now, text segmentation methods have exclusively addressed data sets lying within the scope of narrative and expository texts or user dialogues texts and sometimes artificially generated data @xcite @xcite . in this paper, we address transcriptions of ancient devotional manuscripts (thereafter also "ms"), from the middle ages, known as "books of hours". books of hours were used by lay people as a guidance in their daily prayers. they represent an important source of information on the late middle ages' religious and social practices, and provide opportunities for historical analysis in order to better understand the cultures and faiths of the european society. more than 10,000 manuscripts of ca. 300 pages in average are preserved: they are a specific, standardized, and proto-industrial production @xcite . however, their textual content is still scarcely studied, because of the lack of transcriptions, the complexity of their liturgical content and their standard appearance. they look very uniform at first glance, but each of them is unique. the content differ from one copy to the other as a large choice of devotional readings are available. conversely, some prayers are used in several hours of the day and several times within one copy, thus generating section ambiguities. for historians, automating the generation of table of contents is key to understand this complex historical source. while the building block of the mainstream text segmentation methods is closely related to topical shifts, the liturgical aspect of books of hours exhibits shallow topical relations and a strong correlation between their sections and subsections. as consequence, the topical shift hypothesis becomes inconsistent for this type of data, as has been recently shown in @xcite . we address the task of books of hours segmentation as a classification problem and propose a greedy two-step bottom-up approach that achieves significant results on books of hours. ## related work text segmentation is the task of splitting documents into topically coherent fragments for a better text readability and analysis @xcite @xcite . it is also useful in other nlp and ir @xcite applications such as: summarization, document navigation and indexing, passage retrieval, etc. segmentation can be content-based where each topic is characterised by a specific vocabulary and each vocabulary change implies a topic change @xcite . it can also use topic markers @xcite whether (i) oral: such as prosody, silence; (ii) written: using connectors, introductory expressions or (iii) visual: using line breaks, bullets, numbering, bold, etc. a broad range of unsupervised approaches exploit lexical cohesion to detect coherent segments @xcite thanks to term repetitions @xcite , semantic relations using lexical chains @xcite , dictionary @xcite , collocation networks @xcite , or patterns of lexical co-occurrences @xcite such as discourse structures @xcite . early unsupervised methods include: texttiling, a tf cosine based approach @xcite , lcseg, based on lexical chains @xcite , u00, a probabilistic dynamic programming approach @xcite , topictiling, a topic modeling approach based on latent dirichlet analysis (lda) @xcite . @xcite proposed a semantic relatedness graph approach that exploits word embeddings. @xcite and @xcite studied the contribution of word embeddings on classical segmentation approaches. text segmentation has also been addressed as a multi-document segmentation problem. @xcite for instance, proposed a method for shared topic detection and topic segmentation of multiple similar documents based on weighted mutual information, while @xcite proposed an unsupervised bayesian approach that models both shared and document-specific topics. supervised approaches have also modeled semantic cohesion. some methods performed segmentation at the sentence level to discover elementary discourse units (edu) @xcite while others focused on dialogue. neural network approaches have also been applied such as: texttiling-like embedding approach for query-reply dialogue segmentation @xcite , multi-party dialogue for edu using sequential model @xcite and reinforcement learning @xcite . recently, @xcite proposed segbot, a bidirectional rnn coupled with a pointer network that addresses both topic segmentation and edu. also, lstm or cnn based approaches have been proposed, for instance through bidirectional layers @xcite , sentence embedding-based with four layers bidirectional lstm @xcite or through two symmetric cnn @xcite , etc. finally, @xcite proposed sector, the first lstm-based architecture that combines topical (latent semantic content) and structural information (segmentation) as a mutual task. from a resource perspective, the used data sets were mainly linear. a sequential analysis of topical changes was usually applied @xcite . expository and narrative texts such as stargazer @xcite newspapers @xcite or more general interest articles @xcite as well as synthetic data sets @xcite were often used. later on, data sets with hierarchical structure were addressed, which required a more fine-grained subtopic structure analysis @xcite . @xcite proposed one of the first approaches for hierarchical text segmentation: a supervised agglomerative bottom-up clustering method exploiting paragraph hierarchy. a pioneer unsupervised approach for hierarchical text segmentation was introduced by @xcite using a bayesian generative model with dynamic programming. also, @xcite proposed a clustering algorithm based on topical trees to perform hierarchical segmentation. recently, several data sets have been published showing various types of structures: artificial added to automatic speech recognition transcripts of news videos @xcite , encyclopedic reflecting wikipedia article structure @xcite or topical in goal-oriented dialogues @xcite . our data set encompasses books of hours, each of them with a complex original structure described in the following section. ## books of hours books of hours constitute a challenging data set. theirs is a complex hierarchical entangled structure, of interest both for nlp and for historians. they appeared in the 13th century in france, low countries, england and later on in many other european countries and became by far the best selling book in the middle ages. they include and organize thousands of orations, chants and readings. some of them are common to all books, but others depend on patron and on gender, geographical location, preferred saints and prayers of the commissioner @xcite . most texts are in latin, but some translated from hebrew (psalms) or greek (gospel lessons), or written in different periods for distinct purposes (chant, meditation...). in the manuscripts, the texts are not grouped according to their origin or linguistic features, but deliberately mixed to compose an ensemble. the textual contents are barely studied, although books of hours have been intensively studied by art historians for their many and often gorgeous miniature paintings and decorations @xcite . ## conclusion the present research evidences that hierarchical text segmentation can be achieved for medieval manuscripts and that our models can overcome the difficulties of both an error-prone text and a very correlated text with many repetitions. from the historians perspective, the results are already deemed very useful. the level of expertise and time required for manual annotation are very high. even if some shifts are slightly misplaced (sometimes a few lines above or below), users can rely on the automated creation of tables of contents, pointing correctly to the right page (and not to the line), as is usual in medieval studies. historians are already enthusiastic about these results, because this text segmentation is applied for the first time to medieval manuscripts, based on htr and not on time-consuming scholarly edition. combined with an expected increase in htr accuracy, the proposed hierarchical text segmentation approach supports textual criticism and cultural studies.