8 citations · 11 across the 4 of their papers we have counts for
5 papers
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data. While most exis…
On Generating Extended Summaries of Long Documents
Sajad Sotudeh, Arman Cohan, Nazli Goharian
Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it…
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Sajad Sotudeh, Tong Xiang, Hao-Ren Yao +4
Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes thre…
Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
Sajad Sotudeh, Nazli Goharian, Ross W. Filice
Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively ide…
Ontology-Aware Clinical Abstractive Summarization
Sean MacAvaney, Sajad Sotudeh, Arman Cohan +3
Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abst…