Text-2- W iki: Summarization and Template-driven Article Generation (2023)

Abstract: Users on Wikipedia collaborate in a structured and organized manner to publish and update articles on numerous topics, which makes Wikipedia a very rich source of knowledge. English Wikipedia has the most amount of information available (more than 6.7 million articles); however, there are few good informative articles on Wikipedia in Indian languages. Hindi Wikipedia has approximately only 160k articles. The same article in Hindi can be vastly different from its English version and generally contains less information. This poses a problem for native Indian language speakers who are not proficient in English. Therefore, having the same amount of information in Indian Languages will help promote knowledge among those who are not well-versed in English. Publishing the articles manually, like the usual process in Global English Wikipedia, is a timeconsuming process. To get the amount of information in native Indian languages up-to-speed with the amount of information in English, automating the whole article generation process is the best option. In this study, we present a stage-wise approach ranging from Data Collection to Summarization and Translation, and finally ending with Template Creation. This approach ensures the efficient generation of a large amount of content in Hindi Wikipedia in less time. With the help of this study, we were able to successfully generate more than a thousand articles in Hindi Wikipedia with ease.

## introduction in our study, we aim to populate our native language, i.e. hindi's wikipedia, on a certain domain by making use of a summarization and templatedriven approach. we have selected diseases and medical conditions as our domain as they are a very important topic for readers of any language. the gist of the study is to scrape and collect required data from online resources and then clean it accordingly. then, we summarize the scraped data and organize it to give it some structure. we follow it with translation and transliteration of the data to hindi. this stage involves both machine and manual translation and transliteration. finally, we end the study by drafting up a template in which the data can be filled as fill-in-the-blanks. this ensures the automatic generation of a large amount of articles in accordance with the wiki structure. ## system overview the system for our study can be broken up into 5 different stages. firstly, we scrape relevant information from credible online sources, and then we proceed to clean the scraped unstructured data. after giving it some structure, we proceed to shorten the length of content by summarizing relevant information. then, we proceed to translate the text into hindi, and finally, we generate thousands of wiki articles automatically by using a static template with variations. ## results finally, the xml dump for all 1154 articles was generated and shared with the coordinators. the articles were imported onto the tewiki website without any issues and can be found here. the criteria for evaluation is entirely manual. the articles are reviewed by the editors manually and quite thoroughly. it is a lengthy process which takes time. the small errors relating to the manual aspect of the methodology, for example, transliteration, were corrected manually. apart from those, there were no major concerns or issues raised as such, the articles were incorporated into the tewiki website and, in due time, will be uploaded to global hindi wikipedia as well. if the reviewers may have missed some errors, it would not be a major concern as the wikipedia platform is meant for users to not only read the articles but also help improve them by pointing out mistakes and correcting them manually. in terms of the amount of information, the generated articles had more information than their global hindi wikipedia counterparts and, in some cases, even more than global english wikipedia pages. this is primarily due to the fact that our sources for information were nhs and mayo clinic medical articles, and they many a time contain more information on a particular disease than any global english wikipedia article as they are authored by certified medical professionals and other subject matter experts. finally, we consider the contribution of our study to the wikipedia platform. as discussed earlier, hindi wikipedia has only a fraction of the amount of articles of that of english wikipedia. the same holds true for the "human diseases and disorders" category. english wikipedia has approximately 1500 articles on the subject, while hindi wikipedia's count comes to around 500 articles. with our contribution, this number will rise to a little over a thousand articles. even if we consider 500 articles to be repeated in both versions, our contribution still adds more than 600 articles while also adding extra information to about 500 persisting articles in hindi wikipedia. this clearly highlights that the automated generation of wikipedia articles can help close or at least shorten the vast gap that exists between information available in english and native indian languages. ## conclusion through this study, we were able to explore the effectiveness of the summarization and templatedriven techniques when it comes to the automated generation of a large amount of wikipedia articles. we demonstrated a step-by-step approach to convert unstructured textual data to some form of a mixture of structured and unstructured data and ultimately utilizing translation libraries to enrich the indian language wikipedia. this goes on to show that the techniques indeed complement each other in a good way. with this study, we are able to demonstrate a very important application of nlp techniques in the modern day, i.e., producing a large quantity of good quality content in the native language of the readers on a platform which follows strict guidelines when it comes to the neutral tone of the articles. this method may be used to generate wiki articles in any number of languages, given the availability of required machine translation and transliteration tools and expert assistance for that language.