Franck Dernoncourt

TL;DR: This work proposes the first model for abstractive summarization of single, longer-form documents (e.g., research papers), consisting of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary.

TL;DR: This article proposed a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts for dialog act prediction. But this model is not suitable for short texts and it cannot handle many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding sentences when classifying a subsequent one.

TL;DR: The authors propose a hierarchical encoder that models the discourse structure of a document and an attentive discourse-aware decoder to generate the summary, which significantly outperforms state-of-the-art models.

TL;DR: The first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems, is introduced, which outperforms the state-of-the-art systems.

TL;DR: The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated, and the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.