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. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.

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 retrieval. This study provides empirical evidence that such models can significantly improve retrieval performance, and introduces a new extrinsic evaluation framework that allows for a better understanding of the limitations of keyphrase generation models. Using this framework, we point out and discuss the difficulties encountered with supplementing documents with -not present in text- keyphrases, and generalizing models across domains. Our code is available at https://github.com/boudinfl/ir-using-kg

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 available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https://github.com/ygorg/KPTimes.

Les termes-clés facilitent la recherche de documents dans de larges collections de données. Le coût d’annotation de document en termes-clés très élevé, c’est pourquoi les chercheurs s’intéressent à cette problématique. Dans cet article nous présentons un état de l’art sur l’extraction automatique de termes-clés en nous intéressant particulièrement aux modèles d’apprentissage profond. En effet, la récente publication d’un demi-million de documents annotés à permis le développement de modèles neuronaux profonds.

Nous présentons dans cet article la participation de l’équipe TALN du LS2N à la tâche d’indexation de cas cliniques (tâche 1). Nous proposons deux systèmes permettant d’identifier, dans la liste de mots-clés fournie, les mots-clés correspondant à un couple cas clinique/discussion, ainsi qu’un classifieur entraîné sur la combinaison des sorties des deux systèmes. Nous présenterons dans le détail les descripteurs utilisés pour représenter les mots-clés ainsi que leur impact sur nos systèmes de classification.