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        <title>Extending Neural Keyword Extraction with TF-IDF tagset matching</title>
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        <namePart type="given">Boshko</namePart>
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    <name type="personal">
        <namePart type="given">Blaž</namePart>
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    <name type="personal">
        <namePart type="given">Matej</namePart>
        <namePart type="family">Martinc</namePart>
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            <title>Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation</title>
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            <namePart type="given">Hannu</namePart>
            <namePart type="family">Toivonen</namePart>
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            <namePart type="given">Michele</namePart>
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    <abstract>Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work, we develop and evaluate our methods on four novel data sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.</abstract>
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%0 Conference Proceedings
%T Extending Neural Keyword Extraction with TF-IDF tagset matching
%A Koloski, Boshko
%A Pollak, Senja
%A Škrlj, Blaž
%A Martinc, Matej
%Y Toivonen, Hannu
%Y Boggia, Michele
%S Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F koloski-etal-2021-extending
%X Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work, we develop and evaluate our methods on four novel data sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.
%U https://aclanthology.org/2021.hackashop-1.4
%P 22-29

Markdown (Informal)

[Extending Neural Keyword Extraction with TF-IDF tagset matching](https://aclanthology.org/2021.hackashop-1.4) (Koloski et al., Hackashop 2021)

• Extending Neural Keyword Extraction with TF-IDF tagset matching (Koloski et al., Hackashop 2021)

ACL

• Boshko Koloski, Senja Pollak, Blaž Škrlj, and Matej Martinc. 2021. Extending Neural Keyword Extraction with TF-IDF tagset matching. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 22–29, Online. Association for Computational Linguistics.