activity
20192024
most citedKeyphrase Generation for Scientific Document Retrieval

34 citations · 34 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical Characteristics

Anas Belfathi, Ygor Gallina, Nicolas Hernandez +2

Recent advances in pre-trained language modeling have facilitated significant progress across various natural language processing (NLP) tasks. Word masking during model training co…

cs.IR2021★ 34 cited

Keyphrase Generation for Scientific Document Retrieval

Florian Boudin, Ygor Gallina, Akiko Aizawa

Sequence-to-sequence models have lead to significant progress in keyphrase generation, but it remains unknown whether they are reliable enough to be beneficial for document retriev…

cs.IR2021

Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness

Florian Boudin, Ygor Gallina

Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text…

cs.IR2020

Large-Scale Evaluation of Keyphrase Extraction Models

Ygor Gallina, Florian Boudin, Béatrice Daille

Keyphrase extraction models are usually evaluated under different, not directly comparable, experimental setups. As a result, it remains unclear how well proposed models actually p…

cs.IR2019

KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents

Ygor Gallina, Florian Boudin, Béatrice Daille

Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily av…