| Challenge: | Existing datasets for text segmentation are small in size and do not represent the natural distribution of text in documents. |
| Approach: | They propose a large dataset for text segmentation that is automatically extracted and labeled from Wikipedia and develop a model based on this dataset. |
| Outcome: | The proposed model generalizes well to unseen natural text. |
Similar Papers
Recent Trends in Linear Text Segmentation: A Survey (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Linear text segmentation is the task of automatically tagging text documents with topic shifts . the task is based on coherence modeling and/or local cues to identify topic boundaries . |
| Approach: | They provide an overview of current advances in linear text segmentation . they highlight limitations of available resources and of the task itself . |
| Outcome: | The proposed task is based on the most recent literature and under-explored research directions. |
Investigating the Working of Text Classifiers (C18-1)
Copied to clipboard
| Challenge: | Text classification is one of the most widely studied tasks in natural language processing. |
| Approach: | They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy . |
| Outcome: | The proposed models learn to compose the meaning of the sentences or focus on key lexicons for classifying the document. |
A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
Copied to clipboard
Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
| Challenge: | Existing methods to segment unformatted text and transcripts explicitly train to predict segment boundaries, but they fail to provide a large annotated dataset. |
| Approach: | They propose a method to generate hierarchical segmentation structures based on Wikipedia annotations by using a neural conditional random field. |
| Outcome: | The proposed method outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms. |
Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge. |
| Approach: | They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences. |
| Outcome: | The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation. |
Text Segmentation by Cross Segment Attention (2020.emnlp-main)
Copied to clipboard
| Challenge: | Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents. |
| Approach: | They propose three transformer-based NLP models that break up text into constituents and compare them to previous approaches. |
| Outcome: | The proposed architectures reduce errors by a large margin on three datasets and improve performance on real-world datasets. |
A Joint Model for Document Segmentation and Segment Labeling (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to text segmentation focus on document segmentation and segment labeling separately. |
| Approach: | They propose a method for jointly segmenting a document and labeling segments . they show that S-LSTM reduces segmentation error by 30% on average . |
| Outcome: | The proposed method reduces segmentation error by 30% while improving segment labeling. |
Learning with Limited Text Data (2022.acl-tutorials)
Copied to clipboard
| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
Copied to clipboard
| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
Copied to clipboard
| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |
Assessing the State of the Art in Scene Segmentation (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in scene segmentation have made it difficult to detect scenes in literary texts. |
| Approach: | They propose to modify existing models to improve detection of scenes in literary texts . they propose to use a training sample generation scheme to alleviate this problem . |
| Outcome: | The proposed model is more robust to different types of texts, while its overall performance is slightly worse than that of BERT-based models. |