We introduce and tackle the problem of automatically generating short descriptions of Wikipedia articles (e.g., Belgium has a short description Country in Western Europe). We introduce Descartes, a model that can generate descriptions performing on par with human editors. Our human evaluation results indicate that Descartes is preferred over editor-written descriptions about 50% of time. Further manual analysis show that Descartes generates descriptions considered as "valid" for 91.3% of articles, this is the as same editor-written descriptions. Such performances are made possible by integrating other signals naturally existing in Wikipedia: (i) articles about the same entity in different languages, (ii) existing short descriptions in other languages, and (iii) structural information from Wikidata. Our work has direct practical applications in helping Wikipedia editors to provide short descriptions for the more than 9 million articles still missing one. Finally, our proposed architecture can easily be re-purposed to address other information gaps in Wikipedia.

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to $$50\%$$ 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

No abstract