Filling Gaps in W ikipedia: Leveraging Data-to-Text Generation to Improve Encyclopedic Coverage of Underrepresented Groups (2024)

Abstract: Wikipedia is known to have systematic gaps in its coverage that correspond to under-resourced languages as well as underrepresented groups. This paper presents a new tool to support efforts to fill in these gaps by automatically generating draft articles and facilitating post-editing and uploading to Wikipedia. A rule-based generator and an input-constrained LLM are used to generate two alternative articles, enabling the often more fluent, but error-prone, LLM-generated article to be content-checked against the more reliable, but less fluent, rule-generated article.

## introduction knowledge equity is one of two strategic directions in wikimedia's @xcite . systematic content gaps identified in the wikimedia knowledge gap taxonomy 1 relate e.g. to gender, recency, geography and language. for instance, women and non-binary people make up less than 20% of biographies on wikipedia. 2 addressing such gaps is important for equity and knowledge completeness, but despite increasing awareness, existing social, political and technical barriers still make it difficult to motivate and/or enable wikipedia editors to fill them in. natural language generation (nlg) has held the ambition to help address such gaps for some time @xcite @xcite @xcite , and recent work has shown it to be a valid approach @xcite . however, data-to-text nlg has a high knowledge threshold, and the more recent llm-based nlg systems require substantial cost and energy. with the tool we report in this paper we aim to address the above issues, targeting underresourced languages and the knowledge threshold in particular. our wikipedia gap filler tool generates a draft article in irish or english from an entity name, making it far easier for users to create texts about given entities that can be used as a starting point for a new wikipedia page. the contributions of this paper are: (i) the implementation and release of a tool for generating and editing text snippets on a queried entity, with a human-friendly user interface that integrates all the components of the system; (ii) the implementation and release of code for retrieving information about queried entities on dbpedia and wikidata. the tool and source code can be found on github. 4 2 background and tool overview @xcite provided first indications that using nlg is a good strategy for filling in wikipedia knowledge gaps. in particular they concluded that (i) machine translation is not adequate to create new wikipedia pages, due to the cultural differences between the communities that speak different languages (i.e. each community has their specific points of interest; in addition, source text is not always available); (ii) providing wikipedia editors with even just a starting sentence as in kaffee et al.'s experiments can have a real impact on page editing; (iii) text is judged more useful than tables with raw data such as article stubs @xcite ; and (iv) the main limitation of using nlg for creating text snippets is the lack of factual accuracy of the produced text. the wikipedia gap filler tool builds upon these ideas and goes beyond, generating the first sentence that can then be expanded upon to form a more extensive draft articles generated from a user-selected set of knowledge triples. moreover, the tool addresses issues around factual accuracy by generating two texts in parallel, one using a rule-based generator and the other using an input-constrained llm, enabling the more fluent output from the latter to be content-checked against the more reliable output from the former. finally, our tool provides users with a selection of entities from known gaps, encouraging them to create pages for these underrepresented entities. initiatives such as the wikiproject women in red (wir) 5 aim to create wikipedia content about women's biographies, women's works and women's issues. lists of women who have no wikipedia page, i.e. whose name on wikipedia appears as a red link (hence the name of the initiative), were compiled by the contributors and sorted by category, 6 with 224 categories containing entities for which data is available on wikidata. that is, for about 400,000 women in red, some information is available in the form of triples on (at least) wikidata, which constitute the input to our wikipedia gap filler tool. we take advantage of this resource to suggest names of wir to users of our tool. to use the tool, the user simply enters or selects an entity name and is then offered a list of triples (<entity, property, value>, e.g. <barack_obama, birthyear, 1961>) retrieved from dbpedia or wikidata, from which those to be verbalised in the article can be selected. a wide range of entities can be queried, such as persons, places, buildings, organisations, artistic or intellectual works, fictional characters, vehicles, etc. by using only dbpedia and wikidata contents as input, the contents of the generated texts are fully traceable, and the length of the text is customisable, by selecting more properties in the input or fewer. the user can request one or more text(s) to be generated, before editing the results in the interface, and from there, create a new wikipedia page or enrich an existing one. ## interface the wikipedia gap filler app is implemented with a python flask back-end and a react javascript front-end. the user interface is intended to be very simple to use. first, to input an entity, the user has two alternatives: (i) type in an entity, or (ii) select a suggested entity from the woman in red frame. ## components in this section, we briefly describe the main components of our demo: interactive content selection, text generators, and text editing box. ## limitations some of the limitations of our tool are due to the early stage of development that it is in, but others are more general. for the first type, the tool is currently limited to english and irish texts, and to a subset of about 14% of the dbpedia properties and 1% of the wikidata properties. this is due to the current coverage of the rule-based generator. the more general limitations are two-fold: (i) our tool fully depends on the availability of triples for the queried entity, but there can be very little or no information on dbpedia and wikidata for some under-represented entities; and (ii) despite the help that nlg and llms can bring when creating new pages, there still are challenges and potential issues such as the introduction of societal biases and factual errors @xcite , the attribution of the generated contents @xcite , or the actual impact of these technologies on wikipedia edition @xcite . finally, the present tool is not intended to be used in an unsupervised manner, and the potential users are expected to carefully check that the contents they eventually upload to wikipedia are correct and conform to the wikipedia guidelines (see section 4.3).