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This paper describes the participation of the Artificial Intelligence and Information Retrieval (AIIR) Lab from the University of Southern Maine and the Laboratory of Artificial Intelligence and Decision Support (LIAAD) lab from INESC TEC in the CLEF 2023 SimpleText lab. There are three tasks defined for SimpleText: (T1) What is in (or out)?, (T2) What is unclear?, and (T3) Rewrite this!. Five runs were submitted for Task 1 using traditional Information Retrieval, and Sentence-BERT models. For Task 2, three runs were submitted, using YAKE! and KBIR keyword extraction models. Finally, for Task 3, two models were deployed, one using OpenAI Davinci embeddings and the other combining two unsupervised simplification models.

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Our civilization creates enormous volumes of digital data, a substantial fraction of which is preserved and made publicly available for present and future usage. Additionally, historical born-analog records are progressively being digitized and incorporated into digital document repositories. While professionals often have a clear idea of what they are looking for in document archives, average users are likely to have no precise search needs when accessing available archives (e.g., through their online interfaces). Thus, if the results are to be relevant and appealing to average people, they should include engaging and recognizable material. However, state-of-the-art document archival retrieval systems essentially use the same approaches as search engines for synchronic document collections. In this article, we develop unique ranking criteria for assessing the usefulness of archived contents based on their estimated relationship with current times, which we call contemporary relevance. Contemporary relevance may be utilized to enhance access to archival document collections, increasing the likelihood that users will discover interesting or valuable material. We next present an effective strategy for estimating contemporary relevance degrees of news articles by utilizing learning to rank approach based on a variety of diverse features, and we then successfully test it on the New York Times news collection. The incorporation of the contemporary relevance computation into archival retrieval systems should enable a new search style in which search results are meant to relate to the context of searchers' times, and by this have the potential to engage the archive users. As a proof of concept, we develop and demonstrate a working prototype of a simplified ranking model that operates on the top of the Portuguese Web Archive portal (arquivo.pt).

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The Second International Workshop on Narrative Extraction from Texts (Text2Story'19 [http://text2story19.inesctec.pt/]) was held on the 14th of April 2019, in conjunction with the 41 st European Conference on Information Retrieval (ECIR 2019) in Cologne, Germany. The workshop provided a platform for researchers in IR, NLP, and design and visualization to come together and share the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited talks, ten research paper presentations, and a poster and demo session. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-2342/

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