8 citations · 18 across the 9 of their papers we have counts for
11 papers
Learning to Rank Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. Using a…
QontSum: On Contrasting Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. The broader field of Generative Informa…
Curriculum-Guided Abstractive Summarization
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent Transformer-based summarization models have provided a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal w…
Curriculum-guided Abstractive Summarization for Mental Health Online Posts
Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy +1
Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely respons…
MentSum: A Resource for Exploring Summarization of Mental Health Online Posts
Sajad Sotudeh, Nazli Goharian, Zachary Young
Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health condit…
TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation
Sajad Sotudeh, Nazli Goharian
Many scientific papers such as those in arXiv and PubMed data collections have abstracts with varying lengths of 50-1000 words and average length of approximately 200 words, where…
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…