241 citations · 802 across the 37 of their papers we have counts for
29 papers · 1 filter
Genetic Approach to Mitigate Hallucination in Generative IR
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Generative language models hallucinate. That is, at times, they generate factually flawed responses. These inaccuracies are particularly insidious because the responses are fluent…
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…
ToxCCIn: Toxic Content Classification with Interpretability
Tong Xiang, Sean MacAvaney, Eugene Yang +1
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans. Explanations are particular…
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…
ABNIRML: Analyzing the Behavior of Neural IR Models
Sean MacAvaney, Sergey Feldman, Nazli Goharian +2
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search. However, it is not yet well-understood why these methods ar…
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
Sean MacAvaney, Arman Cohan, Nazli Goharian
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinici…
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
Michael Kranzlein, Shabnam Behzad, Nazli Goharian
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique c…
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…
Interaction Matching for Long-Tail Multi-Label Classification
Sean MacAvaney, Franck Dernoncourt, Walter Chang +2
We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be u…
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…
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Arman Cohan, Bart Desmet, Andrew Yates +3
Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for l…
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Sean MacAvaney, Bart Desmet, Arman Cohan +4
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the tempo…
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim +4
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single,…
Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions
Luca Soldaini, Timothy Walsh, Arman Cohan +2
In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful…
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification
Sean MacAvaney, Luca Soldaini, Arman Cohan +1
SemEval 2018 Task 7 focuses on relation ex- traction and classification in scientific literature. In this work, we present our tree-based LSTM network for this shared task. Our app…
Depression and Self-Harm Risk Assessment in Online Forums
Andrew Yates, Arman Cohan, Nazli Goharian
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter an…
Identifying Harm Events in Clinical Care through Medical Narratives
Arman Cohan, Allan Fong, Raj Ratwani +1
Preventable medical errors are estimated to be among the leading causes of injury and death in the United States. To prevent such errors, healthcare systems have implemented patien…
Scientific document summarization via citation contextualization and scientific discourse
Arman Cohan, Nazli Goharian
The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific document summariz…