title: LongKey: Keyphrase Extraction for Long Documents
summary: In an era of information overload, manually annotating the vast and growing
  corpus of documents and scholarly papers is increasingly impractical. Automated
  keyphrase extraction addresses this challenge by identifying representative
  terms within texts. However, most existing methods focus on short documents (up
  to 512 tokens), leaving a gap in processing long-context documents. In this
  paper, we introduce LongKey, a novel framework for extracting keyphrases from
  lengthy documents, which uses an encoder-based language model to capture
  extended text intricacies. LongKey uses a max-pooling embedder to enhance
  keyphrase candidate representation. Validated on the comprehensive LDKP
  datasets and six diverse, unseen datasets, LongKey consistently outperforms
  existing unsupervised and language model-based keyphrase extraction methods.
  Our findings demonstrate LongKey's versatility and superior performance,
  marking an advancement in keyphrase extraction for varied text lengths and
  domains.
paper_authors._id: 6749a26de60308c9c195f72e
paper_authors.name: Jeovane Honorio Alves
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-29T14:56:01.691Z
paper_authors._id: 6463eb7a31063f253c21fdcc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6463eb7a31063f253c21fdcc/V0hI_xB0HE4qhLieXlPME.jpeg
paper_authors.fullname: Jeovane H Alves
paper_authors.type: user
paper_authors.user: jeohalves
paper_authors._id: 6749a26de60308c9c195f72f
paper_authors.name: Radu State
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-29T12:52:13.681Z
paper_authors._id: 6412db862a07dd87bd30096a
paper_authors.avatarUrl: /avatars/8742da8a6a1e8a1748b9c7dab8329907.svg
paper_authors.fullname: Radu State
paper_authors.type: user
paper_authors.user: RaduState
paper_authors._id: 6749a26de60308c9c195f730
paper_authors.name: Cinthia Obladen de Almendra Freitas
paper_authors._id: 6749a26de60308c9c195f731
paper_authors.name: Jean Paul Barddal
paper_title: LongKey: Keyphrase Extraction for Long Documents
paper_summary: In an era of information overload, manually annotating the vast and growing
  corpus of documents and scholarly papers is increasingly impractical. Automated
  keyphrase extraction addresses this challenge by identifying representative
  terms within texts. However, most existing methods focus on short documents (up
  to 512 tokens), leaving a gap in processing long-context documents. In this
  paper, we introduce LongKey, a novel framework for extracting keyphrases from
  lengthy documents, which uses an encoder-based language model to capture
  extended text intricacies. LongKey uses a max-pooling embedder to enhance
  keyphrase candidate representation. Validated on the comprehensive LDKP
  datasets and six diverse, unseen datasets, LongKey consistently outperforms
  existing unsupervised and language model-based keyphrase extraction methods.
  Our findings demonstrate LongKey's versatility and superior performance,
  marking an advancement in keyphrase extraction for varied text lengths and
  domains.
paper_ai_summary: LongKey, a novel framework using an encoder-based language model and max-pooling embedder, extracts keyphrases from lengthy documents, outperforming existing methods on comprehensive datasets.
paper_ai_keywords: encoder-based language model max-pooling embedder keyphrase extraction LDKP datasets