Summary of Longkey: Keyphrase Extraction For Long Documents, by Jeovane Honorio Alves and Radu State and Cinthia Obladen De Almendra Freitas and Jean Paul Barddal
LongKey: Keyphrase Extraction for Long Documents
by Jeovane Honorio Alves, Radu State, Cinthia Obladen de Almendra Freitas, Jean Paul Barddal
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called LongKey is introduced to automate keyphrase extraction from lengthy documents. Most existing methods focus on short documents, leaving a gap in processing long-context documents. LongKey uses an encoder-based language model to capture extended text intricacies and a max-pooling embedder to enhance keyphrase candidate representation. The method is validated on several datasets, showing consistent outperformance of existing unsupervised and language model-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LongKey is a new way to find important words in long documents. This helps with information overload when we have too many papers to read and annotate. Most current methods can only handle short texts, but LongKey is better at understanding longer texts. It uses special models to learn about the structure of long texts and pick out key phrases. LongKey works well on different types of texts and datasets. |
Keywords
» Artificial intelligence » Encoder » Language model » Unsupervised
