{'fraction_non_alphanumeric': 0.016129032258064516, 'fraction_numerical': 0.0, 'mean_word_length': 5.494186046511628, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards neural abstractive clinical trial text summarization with sequence to sequence models

The recruitment stage in clinical trials is key in ensuring enrollment of a large and diverse number of participants. Recent trends in clinical trials recruitment strategies have leveraged social media, mobile, and web-based platforms to advertise trials to a broader and more diverse set of potential participants. We develop a method to improve clinical trials enrollment rates through novel models of communication that provide accurate and unbiased information about the clinical trials and provide awareness to target participants. The contributions of this paper are two-fold. First we propose a model to generate abstractive summaries for clinical trials based on sequence to sequence networks with attention policies. Second, we present a preliminary evaluation of the model in terms of learning, vocabulary development, choices of attention policies, and summarization outputs. Finally, we generate a dataset consisting of multi-sentence clinical trials summaries to be used for bench-marking and in future work.