Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines.
In this paper, we propose a semi-automatic system for title construction from scientific abstracts. The system extracts and recommends impactful words from the text, which the author can creatively use to construct an appropriate title for the manuscript. The work is based on the hypothesis that keywords are good candidates for title construction. We extract important words from the document by inducing a supervised keyword extraction model. The model is trained on novel features extracted from graph-of-text representation of the document. We empirically show that these graph-based features ar…
Allen Institute for AI（艾伦人工智能研究所）；Bar Ilan University（巴伊兰大学）；Tel Aviv University（特拉维夫大学）
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due …
The University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research（微软研究院）；Army Research Laboratory（陆军研究实验室）
While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the inference results of unigram-based topic models, or utilizes complex n-gram-discovery topic models. These methods generally produce low-quality topical phrases or suffer from poor scalability on even moderately-sized datasets. We propose a different approach that is both computationally efficient and effective. Our…
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工国立科学技术大学）；National Institute for Research and Development in Informatics - ICI Bucharest（布加勒斯特国家信息学研究与发展研究所）；Paris 1 Panthéon-Sorbonne University（巴黎第一大学先贤祠-索邦大学）；University of Bucharest（布加勒斯特大学）
Understanding the complexity of human language requires an appropriate analysis of the statistical distribution of words in texts. We consider the information retrieval problem of detecting and ranking the relevant words of a text by means of statistical information referring to the "spatial" use of the words. Shannon's entropy of information is used as a tool for automatic keyword extraction. By using The Origin of Species by Charles Darwin as a representative text sample, we show the performance of our detector and compare it with another proposals in the literature. The random shuffled text…
Moody’s Analytics（穆迪分析）；IIIT-Delhi（德里英迪拉·甘地信息技术学院）；IIT-Dhanbad（印度理工学院丹巴德分校）；NYU（纽约大学）；Adobe Media Data Science Research（奥多比媒体数据科学研究院）；UT Austin（德克萨斯大学奥斯汀分校）
Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval. Vast majority of the benchmark datasets for this task are from the scientific domain containing only the document title and abstract information. This limits keyphrase extraction (KPE) and keyphrase generation (KPG) algorithms to identify keyphrases from human-written summaries that are often very short (approx 8 sentences). This presents three challenges for real-world applications: human-written summaries are unavailable for most documents, the documents are almost…
China University of Geosciences (Beijing)（中国地质大学（北京））；Shenzhen University（深圳大学）；Tencent AI Lab（腾讯人工智能实验室）
Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitu…
Interpreting Answers to Yes-No Questions in User-Generated Content
Arizona State University（亚利桑那州立大学）；University of Arizona（亚利桑那大学）；Walmart Global Tech（沃尔玛全球技术）；JP Morgan Chase & Co.（摩根大通公司）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
The goal of our project is to develop an accurate tagger for questions posted on Stack Exchange. Our problem is an instance of the more general problem of developing accurate classifiers for large scale text datasets. We are tackling the multilabel classification problem where each item (in this case, question) can belong to multiple classes (in this case, tags). We are predicting the tags (or keywords) for a particular Stack Exchange post given only the question text and the title of the post. In the process, we compare the performance of Support Vector Classification (SVC) for different kern…
Peking University（北京大学）；Harvard University（哈佛大学）；University of California, Berkeley（加州大学伯克利分校）；San Francisco State University（旧金山州立大学）
In this paper we propose a general framework for topic-specific summarization of large text corpora and illustrate how it can be used for the analysis of news databases. Our framework, concise comparative summarization (CCS), is built on sparse classification methods. CCS is a lightweight and flexible tool that offers a compromise between simple word frequency based methods currently in wide use and more heavyweight, model-intensive methods such as latent Dirichlet allocation (LDA). We argue that sparse methods have much to offer for text analysis and hope CCS opens the door for a new branch o…
The formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with ti…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Toronto（多伦多大学）；Academia Sinica（中央研究院）
Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical C…
IBM Research-INDIA（IBM印度研究院）；Delhi Technological University（德里理工大学）；IIIT-Delhi（德里国际信息技术学院）
In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text. We also include a set of challenges we have faced while creating this corpora. In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube. Our Bollywood Movie corpus contains 4000 movies extracted from Wikipedia and 880 trailers extracted from YouTube which were released from 1970-2017. The corpus contains csv files with the following data about each movie - Wikipedia title of m…
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research corpora. However, keywords that researchers choose may occur infrequently, leading to errors that arise from using small samples. In this paper, we use the capacity for memorization, interpolation, and extrapolation of Transformer Language Models such as the GPT series to learn the linguistic behaviors of a subgroup within larger corpora of Yelp reviews. We then use prompt-based queries to generate synthetic text tha…
In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.
Advanced Emotion Intelligence Research (AEIR)（高级情绪智能研究（AEIR））；University of Bath（巴斯大学）
Researchers since at least Darwin have debated whether and to what extent emotions are universal or culture-dependent. However, previous studies have primarily focused on facial expressions and on a limited set of emotions. Given that emotions have a substantial impact on human lives, evidence for cultural emotional relativity might be derived by applying distributional semantics techniques to a text corpus of self-reported behaviour. Here, we explore this idea by measuring the valence and arousal of the twelve most popular emotion keywords expressed on the micro-blogging site Twitter. We do t…
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）；ByteDance AI Lab（字节跳动人工智能实验室）；AdeptMind Research（AdeptMind研究中心）
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated con…
De-identification of clinical free text using natural language processing: A systematic review of current approaches
The Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与发展研究所）；Bayer A.G.（拜耳公司）；School of Computer Science, University of Manchester（曼彻斯特大学计算机科学学院）
GHTM: A graph-based hybrid topic modeling approach for Bengali language
Institute of Information Technology (IIT), University of Dhaka（达卡大学信息技术研究所）；Centre for Advanced Research in Sciences (CARS), University of Dhaka（达卡大学高级科学研究中心）
