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  In contrast with most text-segmentation approach, Estienne is based on token classification. Editorial structure are identified similarly to named-entity recognition.
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- Estienne was trained on 2,000 example of manually annotated texts, excerpted at random from three very large dataset collected by Pleias: Common Corpus (cultural heritage texts in the public domain), Marianne-OpenData (French/English administrative documents) and OpenScientificPile (scientific publications in free licenses, indexed on OpenAlex). Given the diversity of the corpus, Estienne should work out on diverse document formats in European languages.
 
 
 
 
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  Estienne supports the following segmentations:
 
 
 
 
 
 
 
 
 
 
 
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  The model is named in reference to the humanist Henri Estienne who introduced many practices of text segmentation still in use in scholarly edition today.
 
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  In contrast with most text-segmentation approach, Estienne is based on token classification. Editorial structure are identified similarly to named-entity recognition.
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+ Estienne was trained on 2,000 example of manually annotated texts, excerpted at random from three very large dataset collected by Pleias: Common Corpus (cultural heritage texts in the public domain), Marianne-OpenData (French/English administrative documents) and OpenScientificPile (scientific publications in free licenses, indexed on OpenAlex).
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+ Given the diversity of the corpus, Estienne should work out on diverse document formats in European languages.
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+ As Deberta remove newline by default and has no support for it in the tokenizer, they should be replaced by pilcrows (¶)
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  Estienne supports the following segmentations:
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+ * **Text**
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+ * **Separator** - actually a segmentation separator. They are generally based on newline (actually ¶) with some variations due to text segmentation understanding.
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+ * **Title**
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+ * **Table**
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+ * **Dialog** - any kind of speaker attributed intervention.
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+ * **Bibliography** - statement of a specific bibliographic reference, either in a bibliography section or a footnote.
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+ * **Contact** - personal information, can be especially useful in the context of PII removal.
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+ * **Paratext** - any non-meaningful text included in standard documents like header, page numbering, section recall, etc.
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+ * **Author** - author names and signatures.
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+ * **Date** - statement of date and time, common in letters and newspaper articles.
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+ * **Keyword** - list of keywords, especially common in scientific publications.
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  The model is named in reference to the humanist Henri Estienne who introduced many practices of text segmentation still in use in scholarly edition today.