An Analysis of "Capturing Global Informativeness in Open Domain Keyphrase Extraction"
This paper addresses a prevalent challenge in NLP, particularly within the task of KeyPhrase Extraction (KPE) in open-domain scenarios. Traditional neural KPE methods have shown limitations in prioritizing global informativeness over local phraseness. The authors propose a model named JointKPE which leverages pre-trained LLMs to enhance KPE tasks by integrating both local semantic coherence and global informativeness.
Key Contributions
JointKPE introduces an innovative architecture that concurrently targets two critical aspects of keyphrase quality: phraseness and informativeness. While existing neural models often emphasize local context, this model adopts a multi-task learning framework to balance these factors. The paper highlights the ability of JointKPE to handle diverse domains, as showcased by experiments conducted on the OpenKP and KP20k datasets.
Methodology
The JointKPE framework utilizes pre-trained LLMs, like BERT, to generate contextual word embeddings. It employs Convolutional Neural Networks (CNNs) to construct n-gram representations, ensuring that keyphrase candidates retain contextual richness. Importantly, JointKPE estimates the global informativeness of phrases that may occur in varied contexts within a document, using a ranking mechanism facilitated by localized informativeness scores. This is operationalized through the informative ranking loss and keyphrase chunking task, aligning training objectives to promote both phraseness and global document relevance.
Experimental Results
Experiments indicate that JointKPE outperforms both traditional and neural KPE baselines, including state-of-the-art models such as BLING-KPE and CDKGEN. The model successfully improves precision and recall, particularly excelling in extracting long and non-entity keyphrases—areas where previous methods struggled. These results are bolstered by analyzing its behavior on both open-domain and domain-specific datasets, demonstrating robustness across different pre-trained model variants like SpanBERT and RoBERTa.
Implications and Future Work
The implications of JointKPE are significant for advancing open-domain KPE methodologies. By effectively integrating global informativeness into keyphrase extraction, it bridges a crucial gap left by existing approaches that overly focus on local phraseness. This capability potentially enhances downstream NLP tasks such as document summarization and information retrieval, where capturing a broader informational context is critical.
Future research could explore the integration of additional contextual features beyond text, such as multi-modal data from web documents, to further refine informativeness estimates. Additionally, expanding this approach to other languages and domains could provide valuable insights into the model's adaptability and efficacy.
In summary, the paper contributes a methodologically sound and empirically validated approach to enhancing KPE tasks in open-domain scenarios. JointKPE's architecture, focusing on both localized semantic integrity and broader document informativeness, represents a noteworthy advancement in the field of information extraction.