Keyword Extraction

The MPQA Opinion Corpus contains 535 news articles from a wide variety of news sources manually annotated for opinions and other private states (i.e., beliefs, emotions, sentiments, speculations, etc.). Source: http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf Image Source: https://mpqa.cs.pitt.edu/

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

KPTimes is a large-scale dataset of news texts paired with editor-curated keyphrases. Source: KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents

Paper: Improved automatic keyword extraction given more linguistic knowledge Doi: 10.3115/1119355.1119383

We present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396,209 papers. To our knowledge, CSL is the first scientific document dataset in Chinese. Paper | Code and data ## Dataset We obtain the paper's meta-information from the National Engineering Research Center for Science and Technology Resources Sharing Service (NSTR) dated from 2010 to 2020. Then, we filter data by the Catalogue of Chinese Core Journals. According to the Catalogue and collected data, we divide academic fields into 13 first-level categories (e.g., Engineering, Science) and 67 second-level disciplines (e.g., Mechanics, Mathematics). In total, we collect 396,209 instances for the CSL dataset, represented as tuples <T, A, K, c, d>, where T is the title, A is the abstract, K is a list of keywords, c is the category label and d is the discipline label. The paper distribution over categories and the examples of disciplines are shown in below: | Category | #d | len(T) | len(A) | num(K) | #Samples | Discipline Examples | |-----------------|-------------:|-------:|-------:|-------:|----------:|---------------------------------------| | Engineering | 27 | 19.1 | 210.9 | 4.4 | 177,600 | Mechanics,Architecture,Electrical Science | | Science | 9 | 20.7 | 254.4 | 4.3 | 35,766 | Mathematics,Physics,Astronomy,Geography | | Agriculture | 7 | 17.1 | 177.1 | 7.1 | 39,560 | Crop Science,Horticulture,Forestry | | Medicine | 5 | 20.7 | 269.5 | 4.7 | 36,783 | Clinical Medicine,Dental Medicine,Pharmacy | | Management | 4 | 18.7 | 157.7 | 6.2 | 23,630 | Business Management,Public Administration | | Jurisprudence | 4 | 18.9 | 174.4 | 6.1 | 21,554 | Legal Science,Political Science,Sociology | | Pedagogy | 3 | 17.7 | 179.4 | 4.3 | 16,720 | Pedagogy,Psychology,Physical Education | | Economics | 2 | 19.5 | 177.2 | 4.5 | 11,558 | Theoretical Economics,Applied Economics | | Literature | 2 | 18.8 | 158.2 | 8.3 | 10,501 | Chinese Literature,Journalism | | Art | 1 | 17.8 | 170.8 | 5.4 | 5,201 | Art | | History | 1 | 17.6 | 181.0 | 6.0 | 6,270 | History | | Strategics | 1 | 17.5 | 169.3 | 4.0 | 3,555 | Military Science | | Philosophy | 1 | 18.0 | 176.5 | 8.0 | 7,511 | Philosophy | | All | 67 | | | | 396,209 | ## Evaluation Tasks We build a benchmark to facilitate the development of Chinese scientific literature NLP. It contains diverse tasks, ranging from classification to text generation, representing many practical scenarios. We randomly select 100k samples and split the datasets into the training set, validation set and test set according to the ratio, 0.8 : 0.1 : 0.1. This split is shared across different tasks, which allows multitask training and evaluation. Datasets are presented in text2text format. #### 1.Text Summarization (Title Prediction) Predict the paper title from the abstract. Data examples: { "prompt": "to title", "text_a": "多个相邻场景同时进行干涉参数外定标的过程称为联合定标,联合定标能够 \ 保证相邻场景的高程衔接性,能够实现无控制点场景的干涉定标.该文提出了 \ 一种适用于机载InSAR系统的联合定标算法...", "text_b": "基于加权最优化模型的机载InSAR联合定标算法" } #### 2.Keyword Generation Predict a list of keywords from a given paper title and abstract. Data examples: { "prompt": "to keywords", "text_a": "通过对72个圆心角为120°的双跨偏心支承弯箱梁桥模型的计算分析,以梁 \ 格系法为基础编制的3D-BSA软件系统为结构计算工具,用统计分析的方法建 \ 立双跨偏心支承弯箱梁桥结构反应在使用极限状态及承载能力极限状态下与 \ 桥梁跨长... 偏心支承对120°圆心角双跨弯箱梁桥的影响", "text_b": "曲线桥_箱形梁_偏心支承_设计_经验公式" } #### 3.Category Classification Predict the category with the paper title (13 classes). Data examples: { "prompt": "to category", "text_a": "基于模糊C均值聚类的流动单元划分方法——以克拉玛依油田五3中区克下组为例", "text_b": "工学" }, { "prompt": "to category", "text_a": "正畸牵引联合牙槽外科矫治上颌尖牙埋伏阻生的临床观察", "text_b": "医学" } #### 4.Discipline Classification Predict the discipline with the paper abstract (67 classes). Data examples: { "prompt": "to discipline", "text_a": "某铁矿选矿厂所产铁精矿含硫超过0.3%,而现场为了今后发展的需要,要 \ 求将含硫量降到0.1%以下.为此,针对该铁精矿中硫化物主要以磁黄铁矿 \ 形式存在、硫化物多与铁矿物连生且氧化程度较高的特点...", "text_b": "矿业工程" }, { "prompt": "to discipline", "text_a": "为了校正广角镜头的桶形畸变,提出一种新的桶形畸变数字校正方法.它 \ 使用点阵样板校正的方法,根据畸变图和理想图中圆点的位置关系,得出 \ 畸变图像素在X轴和Y轴方向上的偏移量曲面...", "text_b": "计算机科学与技术" }

Dataset contains CS/Math articles abstracts (in Russian) obtained from two online sources. For each article publication year, journal name, authors, title, keyphrases and abstract are provided. Morozov, D. A., Glazkova, A. V., Tyutyulnikov, M. A., & Iomdin, B. L. (2023). Keyphrase generation for abstracts of the Russian-language scientific articles. NSU Vestnik. Series: Linguistics and Intercultural Communication, 21(1), 54-66.

Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present MAKED, a large-scale multi-lingual keyword extraction dataset comprising of 540K+ news articles from British Broadcasting Corporation News (BBC News) spanning 20 languages. It is the first keyword extraction dataset for 11 of these 20 languages. The quality of the dataset is examined by experimentation with several baselines. We believe that the proposed dataset will help advance the field of automatic keyword extraction given its size, diversity in terms of languages used, topics covered and time periods as well as its focus on under-studied languages.