LongKey: Keyphrase Extraction for Long Documents (2411.17863v1)

Abstract: 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 LLM 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 LLM-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.

• The paper introduces a framework that extracts keyphrases from documents with up to 96K tokens by leveraging an encoder-based model.
• It employs a max-pooling mechanism to aggregate contextual information, capturing thematic structures across long texts.
• Validation on LDKP datasets and zero-shot tests displays superior performance over traditional and unsupervised extraction methods.

Overview of "LongKey: Keyphrase Extraction for Long Documents"

The paper "LongKey: Keyphrase Extraction for Long Documents" addresses the challenge of extracting keyphrases from long and complex textual contexts. The focus of this paper is distinct from existing methods that predominantly target short documents of up to about 512 tokens. LongKey introduces a sophisticated framework capable of processing lengthy documents by leveraging an encoder-based LLM, designed to identify keyphrases in extended text sequences effectively.

Key Contributions

1. Long Context Handling: LongKey differentiates itself by supporting up to 96K tokens, enabling it to capture comprehensive context from lengthy documents, which significantly enhances the representational capability of extracted keyphrases compared to traditional models.
2. Innovative Keyphrase Embedding Strategy: Contrary to chunk or sentence-focused methods, LongKey aggregates contextual information across an entire document, thus building a consolidated keyphrase candidate embedding through a max-pooling mechanism. This strategy ensures the extraction process is context-aware and sensitive to variations within a document's thematic structure.
3. Superior Performance: The paper demonstrates LongKey's efficacy through extensive validation on LDKP datasets, which are specifically structured for long-document keyphrase extraction. In these evaluations, LongKey consistently surpasses existing unsupervised and LLM-based techniques.

Experimental Setup and Results

The experiments, conducted using datasets like LDKP3K and LDKP10K, reveal that LongKey yields higher performance metrics compared to predecessor models. Importantly, LongKey's adaptability is further evidenced by competitive results in a zero-shot learning context across six different unseen datasets. This adaptability is a testament to the model's robustness and its potential application in diverse real-world domains.

LongKey also proved effective when evaluated on short documents from well-known datasets such as KP20k and OpenKP, albeit with more moderate improvements. The results suggest that while LongKey excels in processing longer documents, there remains room for enhancements concerning short-text adaptability.

Theoretical and Practical Implications

From a theoretical perspective, LongKey contributes to the broader understanding of text representation in natural language processing. It underscores the importance of comprehensive context handling in textual analysis, especially when dealing with multifaceted content found in large documents.

Practically, LongKey has substantial utility in information retrieval systems, enabling improved indexing and retrieval efficiency by extracting key semantic indicators from extensive texts. Industries that manage large volumes of complex documents, such as legal, academic, and technical fields, stand to gain significant benefits by integrating LongKey into their workflows.

Future Directions

The development of LongKey opens several paths for future research. Exploring the integration with even larger LLMs could further enhance its text comprehension abilities. Additionally, addressing any computational overheads associated with processing long documents might boost its applicability in environments with resource limitations. Finally, expanding its training to incorporate more varied linguistic domains could enhance its generalization capabilities, thereby broadening its scope in multilingual and diverse contextual applications.

In conclusion, LongKey stands out as an advanced framework for keyphrase extraction, offering significant strides in managing long-text documents. Its design underscores the vital interplay between context comprehension and keyphrase relevance, setting a benchmark for future advancements in the field of computational linguistics.