Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

• URL: http://arxiv.org/abs/2001.00891v1
• Date: Fri, 3 Jan 2020 17:06:41 GMT
• Title: Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
• Authors: Goran Glava\v{s} and Swapna Somasundaran
• Abstract summary: We introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones.
• Score: 9.416757363901295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.

Related papers

• Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness [3.2925222641796554]
  "pointer-guided segment ordering" (SO) is a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations. Our experiments show that pointer-guided pre-training significantly enhances the model's ability to understand complex document structures.
  arXiv  Detail & Related papers  (2024-06-06T15:17:51Z)
• From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions [63.11097464396147]
  We introduce a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. We also introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-27T15:59:37Z)
• Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation [80.48979302400868]
  We focus on open vocabulary instance segmentation to expand a segmentation model to classify and segment instance-level novel categories. Previous approaches have relied on massive caption datasets and complex pipelines to establish one-to-one mappings between image regions and captions in nouns. We devise a joint textbfCaption Grounding and Generation (CGG) framework, which incorporates a novel grounding loss that only focuses on matching object to improve learning efficiency.
  arXiv  Detail & Related papers  (2023-01-02T18:52:12Z)
• Toward Unifying Text Segmentation and Long Document Summarization [31.084738269628748]
  We study the role that section segmentation plays in extractive summarization of written and spoken documents. Our approach learns robust sentence representations by performing summarization and segmentation simultaneously. Our findings suggest that the model can not only achieve state-of-the-art performance on publicly available benchmarks, but demonstrate better cross-genre transferability.
  arXiv  Detail & Related papers  (2022-10-28T22:07:10Z)
• HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization [57.798070356553936]
  HETFORMER is a Transformer-based pre-trained model with multi-granularity sparse attentions for extractive summarization. Experiments on both single- and multi-document summarization tasks show that HETFORMER achieves state-of-the-art performance in Rouge F1.
  arXiv  Detail & Related papers  (2021-10-12T22:42:31Z)
• R2D2: Relational Text Decoding with Transformers [18.137828323277347]
  We propose a novel framework for modeling the interaction between graphical structures and the natural language text associated with their nodes and edges. Our proposed method utilizes both the graphical structure as well as the sequential nature of the texts. While the proposed model has wide applications, we demonstrate its capabilities on data-to-text generation tasks.
  arXiv  Detail & Related papers  (2021-05-10T19:59:11Z)
• Syntax-Enhanced Pre-trained Model [49.1659635460369]
  We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. We present a model that utilizes the syntax of text in both pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2020-12-28T06:48:04Z)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)
• Text Segmentation by Cross Segment Attention [2.525236250247906]
  Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents. We establish a new state-of-the-art, reducing in particular the error rates by a large margin in all cases.
  arXiv  Detail & Related papers  (2020-04-30T01:36:52Z)
• Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
  We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
  arXiv  Detail & Related papers  (2020-02-24T12:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.