@inproceedings{giannouris-etal-2024-plain,
    title = "Plain Language Summarization of Clinical Trials",
    author = "Giannouris, Polydoros  and
      Myridis, Theodoros  and
      Passali, Tatiana  and
      Tsoumakas, Grigorios",
    editor = "Nunzio, Giorgio Maria Di  and
      Vezzani, Federica  and
      Ermakova, Liana  and
      Azarbonyad, Hosein  and
      Kamps, Jaap",
    booktitle = "Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.determit-1.6",
    pages = "60--67",
    abstract = "Plain language summarization, or lay summarization, is an emerging natural language processing task, aiming to make scientific articles accessible to an audience of non-scientific backgrounds. The healthcare domain can greatly benefit from applications of automatic plain language summarization, as results that concern a large portion of the population are reported in large documents with complex terminology. However, existing corpora for this task are limited in scope, usually regarding conference or journal article abstracts. In this paper, we introduce the task of automated generation of plain language summaries for clinical trials, and construct CARES (Clinical Abstractive Result Extraction and Simplification), the first corresponding dataset. CARES consists of publicly available, human-written summaries of clinical trials conducted by Pfizer. Source text is identified from documents released throughout the life-cycle of the trial, and steps are taken to remove noise and select the appropriate sections. Experiments show that state-of-the-art models achieve satisfactory results in most evaluation metrics",
}

Plain language summarization, or lay summarization, is an emerging natural language processing task, aiming to make scientific articles accessible to an audience of non-scientific backgrounds. The healthcare domain can greatly benefit from applications of automatic plain language summarization, as results that concern a large portion of the population are reported in large documents with complex terminology. However, existing corpora for this task are limited in scope, usually regarding conference or journal article abstracts. In this paper, we introduce the task of automated generation of plain language summaries for clinical trials, and construct CARES (Clinical Abstractive Result Extraction and Simplification), the first corresponding dataset. CARES consists of publicly available, human-written summaries of clinical trials conducted by Pfizer. Source text is identified from documents released throughout the life-cycle of the trial, and steps are taken to remove noise and select the appropriate sections. Experiments show that state-of-the-art models achieve satisfactory results in most evaluation metrics
Giannouris, Polydoros Myridis, Theodoros Passali, Tatiana Tsoumakas, Grigorios
Plain Language Summarization of Clinical Trials