Search Results for author:
Found 38 papers, 13 papers with code
GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents
A two stage model that additionally includes an abstraction step using BART; and 3.
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts
Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data.
Ranked #1 on
Extreme Summarization
on TLDR9+
Simplified Data Wrangling with ir_datasets
Managing the data for Information Retrieval (IR) experiments can be challenging.
ToxCCIn: Toxic Content Classification with Interpretability
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans.
On Generating Extended Summaries of Long Documents
We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm.
Ranked #1 on
Extended Summarization
on Longsumm Val
ABNIRML: Analyzing the Behavior of Neural IR Models
We present a new comprehensive framework for Analyzing the Behavior of Neural IR ModeLs (ABNIRML), which includes new types of diagnostic tests that allow us to probe several characteristics---such as sensitivity to word order---that are not addressed by previous techniques.
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
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.
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
Interaction Matching for Long-Tail Multi-Label Classification
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 useful for ad-hoc search result ranking.
SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles.
Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task.
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking.
Training Curricula for Open Domain Answer Re-Ranking
We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process.
Expansion via Prediction of Importance with Contextualization
We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches.
Ranking Significant Discrepancies in Clinical Reports
This allows medical practitioners to easily identify and learn from the reports in which their interpretation most substantially differed from that of the attending physician (who finalized the report).
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages.
Ontology-Aware Clinical Abstractive Summarization
Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors.
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Ranked #3 on
Ad-Hoc Information Retrieval
on TREC Robust04
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media.
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Mental health is a significant and growing public health concern.
Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions
In recent years, online communities have formed around suicide and self-harm prevention.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
Ranked #4 on
Unsupervised Extractive Summarization
on Pubmed
Abstractive Text Summarization
Unsupervised Extractive Summarization
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification
SemEval 2018 Task 7 focuses on relation ex- traction and classification in scientific literature.
Depression and Self-Harm Risk Assessment in Online Forums
We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts.
Identifying Harm Events in Clinical Care through Medical Narratives
Preventable medical errors are estimated to be among the leading causes of injury and death in the United States.
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction
Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text.
Scientific document summarization via citation contextualization and scientific discourse
We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure.
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions.
Scientific Article Summarization Using Citation-Context and Article's Discourse Structure
We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model.
A Neural Attention Model for Categorizing Patient Safety Events
Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care.
Triaging Content Severity in Online Mental Health Forums
Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.
Effects of Sampling on Twitter Trend Detection
In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection.
Revisiting Summarization Evaluation for Scientific Articles
Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries.
A Framework for Public Health Surveillance
With the rapid growth of social media, there is increasing potential to augment traditional public health surveillance methods with data from social media.
