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However, due to their complexity and large number of parameters these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents.<\/jats:p>\n          <jats:p>\n            In this article, we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the\n            <jats:sc>Select-And-Rank<\/jats:sc>\n            paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for\n            <jats:sc>Select-And-Rank<\/jats:sc>\n            models utilizing\n            <jats:italic>reparameterizable subset sampling<\/jats:italic>\n            using the\n            <jats:italic>Gumbel-max trick<\/jats:italic>\n            .\n          <\/jats:p>\n          <jats:p>\n            We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are\n            <jats:italic>interpretable by design<\/jats:italic>\n            . Finally, we present real-world applications that benefit from our sentence selection method.\n          <\/jats:p>","DOI":"10.1145\/3576924","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T13:20:32Z","timestamp":1671196832000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Extractive Explanations for Interpretable Text Ranking"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1992-9261","authenticated-orcid":false,"given":"Jurek","family":"Leonhardt","sequence":"first","affiliation":[{"name":"L3S Research Center, Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2486-7608","authenticated-orcid":false,"given":"Koustav","family":"Rudra","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (ISM) Dhanbad, Jharkhand, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0163-0739","authenticated-orcid":false,"given":"Avishek","family":"Anand","sequence":"additional","affiliation":[{"name":"Delft University of Technology, Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"700","volume-title":"Proceedings of the Advances in Neural Information Processing Systems.","volume":"33","author":"Adebayo Julius","year":"2020","unstructured":"Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim. 2020. 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