Search Results for author:
Found 19 papers, 11 papers with code
Extractive Summarization of Long Documents by Combining Global and Local Context
In this paper, we propose a novel neural single document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic.
Ranked #19 on
Text Summarization
on Pubmed
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets.
Systematically Exploring Redundancy Reduction in Summarizing Long Documents
Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents.
Ranked #15 on
Text Summarization
on Pubmed
Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !
The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization.
Predicting Discourse Trees from Transformer-based Neural Summarizers
Previous work indicates that discourse information benefits summarization.
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias.
W-RST: Towards a Weighted RST-style Discourse Framework
Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework.
T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging.
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
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently.
SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds
Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document.
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Content-Controllable Summarization generates summaries focused on the given controlling signals.
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs).
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
The inference-only large language model (ChatGPT) serves as both the generator and editor, with a smaller model acting as the instructor to guide output generation.
Visual Analytics for Generative Transformer Models
While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpretability.
Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack
Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration?
T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging.
KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification
Text classification has wide-ranging applications in various domains.
