Text Segmentation
35 papers with code • 3 benchmarks • 7 datasets
Text segmentation deals with the correct division of a document into semantically coherent blocks.
Benchmarks
These leaderboards are used to track progress in Text Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Datasets
CoNLL
UrduDoc
Most implemented papers
Neural Sequence Segmentation as Determining the Leftmost Segments
LeePleased/LeftmostSeg
•
•
NAACL 2021
Prior methods to text segmentation are mostly at token level.
Sefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network Model
birolkuyumcu/wawunet_for_toxicspan
•
•
SEMEVAL 2021
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection.
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours in total.
Self-supervised Implicit Glyph Attention for Text Recognition
tongkunguan/siga
•
•
CVPR 2023
Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Self-supervised Character-to-Character Distillation for Text Recognition
Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution.
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding
As phrase extraction can be regarded as a $1$D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction.
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
abcpp12383/threestagebinarization
•
•
The efficient segmentation of foreground text information from the background in degraded color document images is a critical challenge in the preservation of ancient manuscripts.
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization
abcpp12383/threestagebinarization
•
•
This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets.
Expanding Scope: Adapting English Adversarial Attacks to Chinese
Most existing studies focused on designing attacks to evaluate the robustness of NLP models in the English language alone.
