TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation

Abstract

The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 6,300 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine 1 and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant subparts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.arXiv:1805.04617v1 [cs.CL] 11 May 2018 6 Topic Modeling and Resource RecommendationAs an example usage of our corpus, we experimented with topic modeling and its extension to 3 http://aclweb.org/anthology/ 4 https://arxiv.org/

TLDR: Extreme Summarization of Scientific Documents

“…Modeling approaches in scientific document summarization include models that exploit citation contexts (Qazvinian et al, 2013;Goharian, 2015, 2017;Zerva et al, 2020), automated survey generation (Mohammad et al, 2009;Jha et al, 2015;Fabbri et al, 2018;Wang et al, 2018), and other techniques focusing on exploiting the unique properties of scientific documents such as long length and structure (Conroy and Davis, 2017;Nikolov et al, 2018;Cohan et al, 2018;Xiao and Carenini, 2019). Yet, such methods have not been studied in the setting of extreme summarization (i.e.…”

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs

“…Prerequisite relation is defined as: If concept A can help understanding concept B, then there is a prerequisite relation from A to B (Gordon et al, 2016). Prerequisite relation has received much attention in recent years (Pan et al, 2017a;Fabbri et al, 2018; and has a direct help for teaching applications. To build prerequisite chains, we first reduce the amount of candidate concept pairs by utilizing taxonomy information (Liang et al, 2015) and video dependency (Roy et al, 2019), and then lead manual annotation.…”

“…Consequently, they are not feasible enough to support ideas that demand more types of information. Moreover, these datasets only contain a small size of specific entities or relation instances, e.g., prerequisite relation of TutorialBank (Fabbri et al, 2018) only has 794 cases, making it insufficient for advanced models (such as graph neural networks).…”

A Summarization System for Scientific Documents

“…For supporting the ACL community, CL Scholar (Singh et al, 2018) presents a graph mining tool on top of the ACL anthology and enables exploration of research progress. Tutori-alBank (Fabbri et al, 2018) helps researchers to learn or stay up-to-date in the NLP field. Recently, paperswithcode 4 is an open resource for ML papers, code and leaderboards.…”