Search Results for author: Nazli Goharian

Found 46 papers, 16 papers with code

GUIR @ MuP 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents

no code implementations sdp (COLING) 2022 Sajad Sotudeh, Nazli Goharian

This paper presents our approach for the MuP 2022 shared task —-Multi-Perspective Scientific Document Summarization, where the objective is to enable summarization models to explore methods for generating multi-perspective summaries for scientific papers.

Document Summarization Scientific Document Summarization

TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users

1 code implementation LREC 2022 Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder

To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has.

Depression Detection

Lexically-Accelerated Dense Retrieval

no code implementations31 Jul 2023 Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder

We introduce 'LADR' (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness.


QontSum: On Contrasting Salient Content for Query-focused Summarization

no code implementations14 Jul 2023 Sajad Sotudeh, Nazli Goharian

Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries.

Answer Generation Contrastive Learning +2

Curriculum-guided Abstractive Summarization for Mental Health Online Posts

no code implementations2 Feb 2023 Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy, Franck Dernoncourt

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 responses to those seeking help for improving their mental state.

Abstractive Text Summarization Extreme Summarization

TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation

1 code implementation NAACL 2022 Sajad Sotudeh, Nazli Goharian

The recent interest to tackle this problem motivated curation of scientific datasets, arXiv-Long and PubMed-Long, containing human-written summaries of 400-600 words, hence, providing a venue for research in generating long/extended summaries.

Extended Summarization

TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts

1 code implementation EMNLP (newsum) 2021 Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt, Nazli Goharian

Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data.

Extreme Summarization

ToxCCIn: Toxic Content Classification with Interpretability

no code implementations EACL (WASSA) 2021 Tong Xiang, Sean MacAvaney, Eugene Yang, Nazli Goharian

Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans.

Classification General Classification

ABNIRML: Analyzing the Behavior of Neural IR Models

2 code implementations2 Nov 2020 Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, Arman Cohan

Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.

Language Modelling

SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search

no code implementations EMNLP 2020 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.


Interaction Matching for Long-Tail Multi-Label Classification

no code implementations18 May 2020 Sean MacAvaney, Franck Dernoncourt, Walter Chang, Nazli Goharian, Ophir Frieder

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.

Classification General Classification +1

SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search

1 code implementation5 May 2020 Sean MacAvaney, Arman Cohan, Nazli Goharian

In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles.

Training Curricula for Open Domain Answer Re-Ranking

1 code implementation29 Apr 2020 Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder

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

1 code implementation29 Apr 2020 Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder

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.

Language Modelling Passage Ranking +2

Ranking Significant Discrepancies in Clinical Reports

no code implementations18 Jan 2020 Sean MacAvaney, Arman Cohan, Nazli Goharian, Ross Filice

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

1 code implementation30 Dec 2019 Sean MacAvaney, Luca Soldaini, Nazli Goharian

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.

Ad-Hoc Information Retrieval Information Retrieval +2

Ontology-Aware Clinical Abstractive Summarization

no code implementations14 May 2019 Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. Filice

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors.

Abstractive Text Summarization

Depression and Self-Harm Risk Assessment in Online Forums

no code implementations EMNLP 2017 Andrew Yates, Arman Cohan, Nazli Goharian

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

no code implementations15 Aug 2017 Arman Cohan, Allan Fong, Raj Ratwani, Nazli Goharian

Preventable medical errors are estimated to be among the leading causes of injury and death in the United States.

Scientific document summarization via citation contextualization and scientific discourse

no code implementations12 Jun 2017 Arman Cohan, Nazli Goharian

We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure.

Document Summarization Scientific Document Summarization +1

Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge

no code implementations23 May 2017 Arman Cohan, Nazli Goharian

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.

Word Embeddings

Scientific Article Summarization Using Citation-Context and Article's Discourse Structure

1 code implementation EMNLP 2015 Arman Cohan, Nazli Goharian

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

no code implementations23 Feb 2017 Arman Cohan, Allan Fong, Nazli Goharian, Raj Ratwani

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

no code implementations22 Feb 2017 Arman Cohan, Sydney Young, Andrew Yates, Nazli Goharian

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

no code implementations LREC 2016 Andrew Yates, Alek Kolcz, Nazli Goharian, Ophir Frieder

In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection.

Revisiting Summarization Evaluation for Scientific Articles

1 code implementation LREC 2016 Arman Cohan, Nazli Goharian

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.

Text Summarization

A Framework for Public Health Surveillance

no code implementations LREC 2014 Andrew Yates, Jon Parker, Nazli Goharian, Ophir Frieder

With the rapid growth of social media, there is increasing potential to augment traditional public health surveillance methods with data from social media.

Information Retrieval

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