The abstract is known to be a promotional genre where researchers tend to exaggerate the benefit of their research and use a promotional discourse to catch the reader's attention. The COVID‐19 pandemic has prompted intensive research and has changed traditional publishing with the massive adoption of preprints by researchers. Our aim is to investigate whether the crisis and the ensuing scientific and economic competition have changed the lexical content of abstracts. We propose a comparative study of abstracts associated with preprints issued in response to the pandemic relative to abstracts produced during the closest pre‐pandemic period. We show that with the increase (on average and in percentage) of positive words (especially effective ) and the slight decrease of negative words, there is a strong increase in hedge words (the most frequent of which are the modal verbs can and may ). Hedge words counterbalance the excessive use of positive words and thus invite the readers, who go probably beyond the ‘usual’ audience, to be cautious with the obtained results. The abstracts of preprints urgently produced in response to the COVID‐19 crisis stand between uncertainty and over‐promotion, illustrating the balance that authors have to achieve between promoting their results and appealing for caution.

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An abstract is not only a mirror of the full article; it also aims to draw attention to the most important information of the document it summarizes. Many studies have compared abstracts with full texts for their informativeness. In contrast to previous studies, we propose to investigate this relation based not only on the amount of information given by the abstract but also on its importance. The main objective of this paper is to introduce a new metric called GEM to measure the "generosity" or representativeness of an abstract. Schematically speaking, a generous abstract should have the best possible score of similarity for the sections important to the reader. Based on a questionnaire gathering information from 630 researchers, we were able to weight sections according to their importance. In our approach, seven sections were first automatically detected in the full text. The accuracy of this classification into sections was above 80% compared with a dataset of documents where sentences were assigned to sections by experts. Second, each section was weighted according to the questionnaire results. The GEM score was then calculated as a sum of weights of sections in the full text corresponding to sentences in the abstract normalized over the total sum of weights of sections in the full text. The correlation between GEM score and the mean of the scores assigned by annotators was higher than the correlation between scores from different experts. As a case study, the GEM score was calculated for 36,237 articles in environmental sciences retrieved from the French ISTEX database. The main result was that GEM score has increased over time. Moreover, this trend depends on subject area and publisher. No correlation was found between GEM score and citation rate or open access status of articles. We conclude that abstracts are more generous in recent publications and cannot be considered as mere teasers. This research should be pursued in greater depth, particularly by examining structured abstracts. GEM score could be a valuable indicator for exploring large numbers of abstracts, by guiding the reader in his/her choice of whether or not to obtain and read full texts.

Abstract. MC2 CLEF 2017 lab deals with how cultural context of a microblog affects its social impact at large. This involves microblog search, classification, filtering, language recognition, localization, entity extraction, linking open data, and summarization. Regular Lab participants have access to the private massive multilingual microblog stream of The Festival Galleries project. Festivals have a large presence on social media. The resulting mircroblog stream and related URLs is appropriate to experiment advanced social media search and mining methods. A collection of 70,000,000 microblogs over 18 months dealing with cultural events in all languages has been released to test multilingual content analysis and microblog search. For content analysis topics were in any language and results were expected in four languages: English, Spanish, French, and Portuguese. For microblog search topics were in four languages: Arabic, English, French and Spanish, and results were expected in any language.

Information retrieval has moved from traditional document retrieval in which search is an isolated activity, to modern information access where search and the use of the information are fully integrated. But non-experts tend to avoid authoritative primary sources such as scientific literature due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. The CLEF 2021 SimpleText track addresses the opportunities and challenges of text simplification approaches to improve scientific information access head-on. We aim to provide appropriate data and benchmarks, starting with pilot tasks in 2021, and create a community of NLP and IR researchers working together to resolve one of the greatest challenges of today.

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The Web and social media have become the main source of information for citizens, with the risk that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. Non-experts tend to avoid scientific literature due to its complex language or their lack of prior background knowledge. Text simplification promises to remove some of these barriers. The CLEF 2022 SimpleText track addresses the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of NLP and IR researchers working together to resolve one of the greatest challenges of today. The track will use a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.

Abstract. This paper presents the approach we developed for automatic multi-document summarization applied to short message contextualization, in particular to tweet contextualization. The proposed method is based on named entity recognition, part-of-speech weighting and sentence quality measuring. In contrast to previous research, we introduced an algorithm from smoothing from the local context. Our approach exploits topic-comment structure of a text. Moreover, we developed a graph-based algorithm for sentence reordering. The method has been evaluated at INEX/CLEF tweet contextualization track. We provide the evaluation results over the 4 years of the track. The method was also adapted to snippet retrieval and query expansion. The evaluation results indicate good performance of the approach.