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Found 13 papers, 7 papers with code
KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification
Text classification has wide-ranging applications in various domains.
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.
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.
Human Interpretation and Exploitation of Self-attention Patterns in Transformers: A Case Study in Extractive Summarization
The transformer multi-head self-attention mechanism has been thoroughly investigated recently.
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
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.
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.
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.
Predicting Discourse Trees from Transformer-based Neural Summarizers
Previous work indicates that discourse information benefits summarization.
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.
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 #9 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.
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 #13 on
Text Summarization
on arXiv
