Data Augmentation for Low-Resource Keyphrase Generation (2023)

Abstract: Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but they still rely on a lot of additional unlabeled data for pretraining and on automatic methods for pseudo-annotations. In this paper, we present data augmentation strategies specifically to address keyphrase generation in purely resource-constrained domains. We design techniques that use the full text of the articles to improve both present and absent keyphrase generation. We test our approach comprehensively on three datasets and show that the data augmentation strategies consistently improve the state-of-the-art performance. We release our source code at https://github.com/kgarg8/kpgen-lowres-data-aug .

## introduction keyphrase generation (kg) helps in document understanding by summarizing the document in the form of a few salient phrases (or keyphrases). these keyphrases may or may not appear verbatim in the original text and accordingly, they are referred to as either present or absent keyphrases. the task has useful applications to many downstream tasks, e.g., document clustering @xcite , matching reviewers to appropriate papers in the conference portals @xcite , recommendation systems @xcite ), text classification @xcite @xcite , index construction @xcite and sentiment analysis and opinion mining @xcite . prior works for keyphrase generation have largely focused on using large-scale annotated datasets for various domains -computer science (kp20k), news (kptimes, jptimes), webpages (openkp), etc. however, such large annotation datasets are not available in all domains (e.g., medicine, law, finance), either due to paucity in terms of available data or lack of domain expertise among the annotators or even the high annotation costs. this necessitates the focus on the low-resource domains. the traditional ways to address low-resource keyphrase generation have been centered around using semi-supervised or unsupervised learning techniques @xcite @xcite . for these methods, a lot of unlabeled data is necessary and needs to be curated for model training. the unlabeled data is further annotated automatically using keyphrase extraction methods and is used for pretraining the model or is used in the auxiliary task for multitasking. there are two limitations to these methods: in this paper, we develop data augmentation strategies for purely low-resource domains, which do not require acquiring unlabeled data for pretraining or automatic annotation approaches for unlabeled data (which may introduce errors). inspired by @xcite who showed the benefits of using information beyond the title and abstract for keyphrase generation, we leverage the full text of the documents (which is often ignored by prior works) and present ways for augmenting the text for improving both present and absent keyphrase generation performance. data augmentation in nlp has recently become a promising line of research to improve the state-ofthe-art performance @xcite @xcite excerpts from different data augmentation methods title || abstract casesian : a knowledge-based system using statistical and experiential perspectives for improving the knowledge sharing in the medical prescription process @xcite objectives : knowledge sharing is crucial for better patient care in the healthcare industry aug_ta_sr casesian : a knowledge based system using statistical and experiential perspectives for better the knowledge sharing in the medical examination prescription @xcite objectives : knowledge sharing is crucial for advantageously patient role care in the healthcare industry aug_ta_bt cassian : a knowledge-based system that uses statistical and experiential perspectives to improve the sharing of knowledge in the medical prescription process [sep] objectives : knowledge sharing is essential to improve patient care in the health sector aug_ta_kpd casesian : a [mask] using statistical and experiential perspectives for improving the [mask] in the [mask] process [sep] objectives : [mask] is crucial for better patient care in the healthcare industry aug_ta_kpsr casesian : a cognition based system using statistical and experiential perspectives for improving the noesis sharing in the checkup prescription process [sep] objectives : noesis sharing is crucial for better patient care in the healthcare industry aug_body numerous methods have been investigated for improving the knowledge sharing process in medical prescription [sep] case-based reasoning is one of the most prevalent knowledge extraction methods gold keyphrases case-based reasoning , medical prescription , knowledge-based system , knowledge sharing , bayesian theorem in addition to augmentation with the body, we also provide methods for augmentation using t || a. we depict the representative augmentation strategies in the intuition is that while augmenting the text if we further drop some of the present keyphrases, similar to masked language modeling @xcite , that makes the task harder and the model is forced to learn to generate the keyphrases. introducing synonyms and back-translation further increases the diversity of the samples in a much controlled way. recently, several full-text datasets have been proposed for the kg task, e.g., fulltextkp @xcite , ldkp3k @xcite , and ldkp10k @xcite . we use two of these datasets, i.e., ldkp3k and ldkp10k, that contain scientific papers, along with a third dataset kptimes @xcite which mimics full-text keyphrase generation datasets but from a different domain, i.e., news. through extensive experiments on the three datasets, we observe that although it is hard to improve the present keyphrase generation performance without sacrificing the absent keyphrase generation performance, our proposed augmentation approaches with the body consistently improve both. moreover, the augmentation methods with body steadily surpass the performance of data augmentation methods that use only title and abstract. in summary, the main contribution of the paper is to demonstrate data augmentation strategies for the keyphrase generation task particularly for purely low-resource domains (which have been under-explored). we present simple yet effective data augmentation methods using the full text of the articles and demonstrate large improvements over the state-of-the-art methods. ## methods in this section, we first describe the formulation of the keyphrase generation task. next, we describe the baselines followed by the data augmentation strategies that we propose for keyphrase generation. problem formulation. keyphrase generation can be posited as a sequence-to-sequence generation task where the input is the text from a given article and the output is a sequence of keyphrases that summarize the article. formally foot_0 , the task can be denoted as follows: ## results and analysis we present our discussion of results for the generation of the two types of keyphrases, i.e., present and absent in §5.1 and §5.2, respectively. ## analysis in this section, we study one of the settings in more detail, i.e., with the ldkp3k dataset having 1000 samples in the training set (and twice the number in the training set for aug-prefixed methods). the study unfolds into two aspects: (a) analyzing the data created for the different augmentation methods, (b) developing better inference strategies. we analyze the data created using the different augmentation methods and report the present, absent and total number of keyphrases in further, in ## conclusion although data augmentation has been a very common practice to advance the state-of-the-art in nlp, it has been under-explored for the keyphrase generation (kg) task. thus, this work discusses various data augmentation methods including both types (i.e., standard and kg-specific) particularly for purely low-resource keyphrase generation, and provides comprehensive evaluation for 12 different settings (four settings for three datasets each). we also leverage the full text of the articles for data augmentation and observe large improvements over the baseline as well as over data augmentation methods that use only title and abstract (t || a). detailed analysis helps us believe that kgspecific data augmentation methods can largely improve absent keyphrase generation but at the cost of present keyphrase generation. in contrast, the standard data augmentation techniques like synonym replacement and back-translation are capable of introducing enough diversity to improve the present keyphrase generation without bringing a drop in absent keyphrase generation performance. although augmentation with the body improves both types of generation to some degree, this work leaves much room to develop better data augmentation strategies to train the model to do better on both present and absent keyphrase generation in low-resource settings which are prevalent in many domains.