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    <titleInfo>
        <title>SECTOR: A Neural Model for Coherent Topic Segmentation and Classification</title>
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    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Arnold</namePart>
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    <name type="personal">
        <namePart type="given">Rudolf</namePart>
        <namePart type="family">Schneider</namePart>
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    <name type="personal">
        <namePart type="given">Philippe</namePart>
        <namePart type="family">Cudré-Mauroux</namePart>
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    <name type="personal">
        <namePart type="given">Felix</namePart>
        <namePart type="given">A</namePart>
        <namePart type="family">Gers</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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    <abstract>When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available data set with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR long short-term memory model with Bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 over state-of-the-art CNN classifiers with baseline segmentation.</abstract>
    <identifier type="citekey">arnold-etal-2019-sector</identifier>
    <identifier type="doi">10.1162/tacl_a_00261</identifier>
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        <url>https://aclanthology.org/Q19-1011</url>
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        <date>2019</date>
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            <start>169</start>
            <end>184</end>
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%0 Journal Article
%T SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
%A Arnold, Sebastian
%A Schneider, Rudolf
%A Cudré-Mauroux, Philippe
%A Gers, Felix A.
%A Löser, Alexander
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F arnold-etal-2019-sector
%X When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available data set with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR long short-term memory model with Bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 over state-of-the-art CNN classifiers with baseline segmentation.
%R 10.1162/tacl_a_00261
%U https://aclanthology.org/Q19-1011
%U https://doi.org/10.1162/tacl_a_00261
%P 169-184

Markdown (Informal)

[SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://aclanthology.org/Q19-1011) (Arnold et al., TACL 2019)

• SECTOR: A Neural Model for Coherent Topic Segmentation and Classification (Arnold et al., TACL 2019)

ACL

• Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, Felix A. Gers, and Alexander Löser. 2019. SECTOR: A Neural Model for Coherent Topic Segmentation and Classification. Transactions of the Association for Computational Linguistics, 7:169–184.