A Large-Scale Dataset for Biomedical Keyphrase Generation (2022)

Abstract: Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp-biomed, the first large-scale biomedical keyphrase generation dataset collected from PubMed abstracts. We train and release several generative models and conduct a series of experiments showing that using large scale datasets improves significantly the performances for present and absent keyphrase generation. The dataset and models are available online.

## introduction keyphrase generation aims at automatically generating a set of keyphrases, that is, words and phrases that summarize a given document. since they distill the important information from documents, keyphrases have showed to be useful in many applications, most notably in information retrieval @xcite @xcite @xcite and summarization @xcite @xcite . current models for generating keyphrases are built upon the sequence-to-sequence architecture @xcite and are able to generate absent keyphrases that is, keyphrases that do not appear in the source text. however, training these models require large amounts of labeled data @xcite . unfortunately, such data is only available for limited domains and languages which greatly limits the applicability of these models @xcite . this work addresses this issue and introduces kp-biomed, the first large-scale dataset for keyphrase generation in the biomedical domain. creating labeled data for keyphrase generation is a challenging task, requiring expert annotators and great effort @xcite . a commonly-used approach to cope with this task is to collect scientific abstracts and use keyphrases provided by authors as a proxy for expert annotations. authors provide keyphrases without any vocabulary constraint to highlight important points of their article; whereas indexers use a specific vocabulary and focus on indexing the article within a collection @xcite . therefore, keyphrases may differ from mesh headings which are another indexing resource in the biomedical domain. fortunately, author keyphrases are becoming increasingly available in the biomedical domain @xcite , since they can be incorporated into search strategies in pubmed to improve retrieval effectiveness @xcite . despite this, the largest keyphrase-labeled biomedical dataset that we know of has about 3k abstracts, all of which are labeled with present-only keyphrases @xcite . in this paper, we take advantage of the expansive pubmed database to build a sufficiently large dataset to train biomedical keyphrase generation models foot_0 . we then compare models trained with different training set sizes to highlight the impact of dataset sizes in keyphrase generation. our contributions are as follows: ## dataset we employ the december 2021 baseline set of medline/pubmed citation records foot_3 as a resource for collecting abstracts, which contains over 33 million records. we extracted all the records (5.9 million) that include a title, an abstract and some author keyphrases. records of papers published between 1939 and 2011 only account for a small fraction of these extracted records (3%) and were further filtered out to avoid possible diachronic issues. last, we went through the remaining records to split the semicolon-separated list of author keyphrases and discard those having keyphrases with punctuation in it. the resulting dataset is composed of 5.6 million abstracts and was randomly and evenly divided by publishing year into training, validation and test splits. to investigate the impact of the amount of training data on the quality of the generated keyphrases, the training split was further divided into increasingly large subsets: small (500k), medium (2m) and large (5.6m). the training splits are also evenly divided by publishing year. statistics of the kp-biomed dataset are detailed in using keyphrases as proxies for indexing or expanding documents with queries composed of words that do not appear in the source text, has been proven more useful to enhance document retrieval than using words occurring in the text @xcite . in keyphrase generation, we call those keyphrases absent keyphrases, for which several definitions are being used. we refer to the definition from @xcite "we denote phrases that do not match any contiguous subsequence of source text as absent keyphrases" which was then precised in @xcite . in @xcite the keyphrase "anesthesia" is considered present if the word "postanesthesia" is in the source text. in our case, it is considered absent which is why namedkeys does not appear with 100% present keyphrases in