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Found 67 papers, 42 papers with code
Overview of the Second Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Overview of the Third Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Overview of the First Shared Task on Multi Perspective Scientific Document Summarization (MuP)
We present the main findings of MuP 2022 shared task, the first shared task on multi-perspective scientific document summarization.
Zero- and Few-Shot NLP with Pretrained Language Models
The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult.
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.
Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?
In this study, we assess the capability of Current LLMs in generating complex structured data and propose a structure-aware fine-tuning approach as a solution to improve this ability.
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries.
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval.
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks.
A Controllable QA-based Framework for Decontextualization
We propose a question-answering framework for decontextualization that allows for better handling of user information needs and preferences when determining the scope of rewriting.
Large Language Models are Effective Table-to-Text Generators, Evaluators, and Feedback Providers
In this paper, we study the capabilities of LLMs for table-to-text generation tasks, particularly aiming to investigate their performance in generating natural language statements that can be logically entailed by a provided table.
QTSumm: A New Benchmark for Query-Focused Table Summarization
People primarily consult tables to conduct data analysis or answer specific questions.
On Learning to Summarize with Large Language Models as References
Therefore, we investigate a new learning paradigm of text summarization models that considers the LLMs as the reference or the gold-standard oracle on commonly used summarization datasets such as the CNN/DailyMail dataset.
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions.
Inference-time Re-ranker Relevance Feedback for Neural Information Retrieval
Neural information retrieval often adopts a retrieve-and-rerank framework: a bi-encoder network first retrieves K (e. g., 100) candidates that are then re-ranked using a more powerful cross-encoder model to rank the better candidates higher.
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs.
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Motivated by our survey, we present LongEval, a set of guidelines for human evaluation of faithfulness in long-form summaries that addresses the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores?
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
Towards multi-document summarization in the open-domain
Multi-document summarization (MDS) traditionally assumes a set of topic-related documents are provided.
Ranked #1 on
Multi-Document Summarization
on MS^2
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
However, existing benchmarks for evaluating these representations fail to capture the diversity of relevant tasks.
SciFact-Open: Towards open-domain scientific claim verification
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic setting against large corpora of scientific literature.
Embedding Recycling for Language Models
Real-world applications of neural language models often involve running many different models over the same corpus.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach.
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.
Long Context Question Answering via Supervised Contrastive Learning
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents.
MultiVerS: Improving scientific claim verification with weak supervision and full-document context
Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments.
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
We present a new scientific document similarity model based on matching fine-grained aspects of texts.
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data.
Ranked #1 on
Multi-Document Summarization
on Multi-News
FLEX: Unifying Evaluation for Few-Shot NLP
Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental design.
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting
In our work, we address this research gap by proposing a novel framework for CCA as a document-level context extraction and labeling task.
Beyond Paragraphs: NLP for Long Sequences
In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for document-level representation learning.
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Readers of academic research papers often read with the goal of answering specific questions.
Ranked #1 on
Question Answering
on QASPER
Simplified Data Wrangling with ir_datasets
Managing the data for Information Retrieval (IR) experiments can be challenging.
CDLM: Cross-Document Language Modeling
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective.
Ranked #1 on
Citation Recommendation
on AAN test
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
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
ABNIRML: Analyzing the Behavior of Neural IR Models
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.
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.
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.
TLDR: Extreme Summarization of Scientific Documents
We introduce TLDR generation, a new form of extreme summarization, for scientific papers.
Fact or Fiction: Verifying Scientific Claims
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.
SPECTER: Document-level Representation Learning using Citation-informed Transformers
We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.
Ranked #1 on
Document Classification
on SciDocs (MAG)
Longformer: The Long-Document Transformer
To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer.
Ranked #2 on
Question Answering
on WikiHop
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).
SUPP.AI: Finding Evidence for Supplement-Drug Interactions
We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions.
Pretrained Language Models for Sequential Sentence Classification
As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document.
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
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.
Ranked #2 on
Sentence Classification
on SciCite
SciBERT: A Pretrained Language Model for Scientific Text
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
Ranked #1 on
Sentence Classification
on Paper Field
(using extra training data)
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.
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.
