%0 Conference Paper %1 Pattisapu_2020 %A Pattisapu, Nikhil %A Prabhu, Nishant %A Bhati, Smriti %A Varma, Vasudeva %B Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval %D 2020 %I ACM %K helper sigir2020 social-media text-complexity %P 851-860 %R 10.1145/3397271.3401105 %T Leveraging Social Media for Medical Text Simplification %U https://doi.org/10.1145%2F3397271.3401105 %X Patients are increasingly using the web for understanding medical information, making health decisions, and validating physicians' advice. However, most of this content is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. Most text simplification methods employ neural seq-to-seq models for this task. However, training such models requires a corpus of aligned complex and simple sentences. Creating such a dataset manually is effort intensive, while creating it automatically is prone to alignment errors. To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text. Experiments on four datasets show that our method significantly outperforms the best known medical text simplification models across multiple automated and human evaluation metrics. Our model achieves an improvement of up to 16.52% over the existing best performing model on SARI which is the primary metric to evaluate text simplification models.

@inproceedings{Pattisapu_2020, abstract = {Patients are increasingly using the web for understanding medical information, making health decisions, and validating physicians' advice. However, most of this content is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. Most text simplification methods employ neural seq-to-seq models for this task. However, training such models requires a corpus of aligned complex and simple sentences. Creating such a dataset manually is effort intensive, while creating it automatically is prone to alignment errors. To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text. Experiments on four datasets show that our method significantly outperforms the best known medical text simplification models across multiple automated and human evaluation metrics. Our model achieves an improvement of up to 16.52% over the existing best performing model on SARI which is the primary metric to evaluate text simplification models. }, added-at = {2020-11-23T00:20:03.000+0100}, author = {Pattisapu, Nikhil and Prabhu, Nishant and Bhati, Smriti and Varma, Vasudeva}, biburl = {https://www.bibsonomy.org/bibtex/2673f17ef262ae02efa6e9a0e713b7203/brusilovsky}, booktitle = {Proceedings of the 43rd International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval}, description = {Leveraging Social Media for Medical Text Simplification | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {10.1145/3397271.3401105}, interhash = {36d2accf6bdd0ffbbb39578a39dcb73d}, intrahash = {673f17ef262ae02efa6e9a0e713b7203}, keywords = {helper sigir2020 social-media text-complexity}, month = jul, pages = {851-860}, publisher = {{ACM}}, timestamp = {2020-11-23T00:20:03.000+0100}, title = {Leveraging Social Media for Medical Text Simplification}, url = {https://doi.org/10.1145%2F3397271.3401105}, year = 2020 }