Descartes: Generating Short Descriptions of Wikipedia Articles

Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all articles should be annotated with a so-called short description indicating the article's topic (e.g., the short description of beer is "Alcoholic drink made from fermented cereal grains"). Nonetheless, a large fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no short description yet, with detrimental effects for millions of Wikipedia users. Motivated by this problem, we introduce the novel task of automatically generating short descriptions for Wikipedia articles and propose Descartes, a multilingual model for tackling it. Descartes integrates three sources of information to generate an article description in a target language: the text of the article in all its language versions, the already-existing descriptions (if any) of the article in other languages, and semantic type information obtained from a knowledge graph. We evaluate a Descartes model trained for handling 25 languages simultaneously, showing that it beats baselines (including a strong translation-based baseline) and performs on par with monolingual models tailored for specific languages. A human evaluation on three languages further shows that the quality of Descartes's descriptions is largely indistinguishable from that of human-written descriptions; e.g., 91.3% of our English descriptions (vs. 92.1% of human-written descriptions) pass the bar for inclusion in Wikipedia, suggesting that Descartes is ready for production, with the potential to support human editors in filling a major gap in today's Wikipedia across languages

PaperRobot: Incremental Draft Generation of Scientific Ideas

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at https://github.com/EagleW/PaperRobo

Wikipedia @ 20

Wikipedia’s first twenty years: how what began as an experiment in collaboration became the world’s most popular reference work. We have been looking things up in Wikipedia for twenty years. What began almost by accident—a wiki attached to a nascent online encyclopedia—has become the world’s most popular reference work. Regarded at first as the scholarly equivalent of a Big Mac, Wikipedia is now known for its reliable sourcing and as a bastion of (mostly) reasoned interaction. How has Wikipedia, built on a model of radical collaboration, remained true to its original mission of “free access to the sum of all human knowledge” when other tech phenomena have devolved into advertising platforms? In this book, scholars, activists, and volunteers reflect on Wikipedia’s first twenty years, revealing connections across disciplines and borders, languages and data, the professional and personal. The contributors consider Wikipedia’s history, the richness of the connections that underpin it, and its founding vision. Their essays look at, among other things, the shift from bewilderment to respect in press coverage of Wikipedia; Wikipedia as “the most important laboratory for social scientific and computing research in history”; and the acknowledgment that “free access” includes not just access to the material but freedom to contribute—that the summation of all human knowledge is biased by who documents it. Contributors Phoebe Ayers, Omer Benjakob, Yochai Benkler, William Beutler, Siko Bouterse, Rebecca Thorndike-Breeze, Amy Carleton, Robert Cummings, LiAnna L. Davis, Siân Evans, Heather Ford, Stephen Harrison, Heather Hart, Benjamin Mako Hill, Dariusz Jemielniak, Brian Keegan, Jackie Koerner, Alexandria Lockett, Jacqueline Mabey, Katherine Maher, Michael Mandiberg, Stephane Coillet-Matillon, Cecelia A. Musselman, Eliza Myrie, Jake Orlowitz, Ian A. Ramjohn, Joseph Reagle, Anasuya Sengupta, Aaron Shaw, Melissa Tamani, Jina Valentine, Matthew Vetter, Adele Vrana, Denny Vrandeči

Characterizing domain-specific open educational resources by linking ISCB Communities of Special Interest to Wikipedia

Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield.We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia

Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge

This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness -- with the winning SLUG system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2seq models often fail to correctly express a given meaning representation if they lack a strong semantic control mechanism applied during decoding. Moreover, seq2seq models can be outperformed by hand-engineered systems in terms of overall quality, as well as complexity, length and diversity of outputs. This research has influenced, inspired and motivated a number of recent studies outwith the original competition, which we also summarise as part of this paper.Comment: Computer Speech and Language, final accepted manuscript (in press

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata.

International audienc

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available