Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark

• URL: http://arxiv.org/abs/2305.14790v2
• Date: Tue, 26 Mar 2024 11:29:21 GMT
• Title: Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark
• Authors: Feng Jiang, Weihao Liu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Haizhou Li,
• Abstract summary: paragraph-level topic structure can grasp and understand the overall context of a document from a higher level. The lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained research and applications. We propose a hierarchical paragraph-level topic structure representation with three layers to guide the corpus construction. We employ a two-stage man-machine collaborative annotation method to construct the largest Chinese paragraph-level Topic Structure corpus.
• Score: 44.06803331843307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragraph-level topic structure can quickly grasp and understand the overall context of the document from a higher level, benefitting many downstream tasks such as summarization, discourse parsing, and information retrieval. However, the lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained relative research and applications. To fill this gap, we build the Chinese paragraph-level topic representation, corpus, and benchmark in this paper. Firstly, we propose a hierarchical paragraph-level topic structure representation with three layers to guide the corpus construction. Then, we employ a two-stage man-machine collaborative annotation method to construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), achieving high quality. We also build several strong baselines, including ChatGPT, to validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) and preliminarily verified its usefulness for the downstream task (discourse parsing).

Related papers

• 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)
• RST-style Discourse Parsing Guided by Document-level Content Structures [27.28989421841165]
  Existing RST parsing pipelines construct rhetorical structures without the knowledge of document-level content structures. We propose a novel pipeline for RST-DP that incorporates structure-aware news content sentence representations.
  arXiv  Detail & Related papers  (2023-09-08T05:50:27Z)
• Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study [51.079100495163736]
  This paper systematically inspects ChatGPT's performance in two discourse analysis tasks: topic segmentation and discourse parsing. ChatGPT demonstrates proficiency in identifying topic structures in general-domain conversations yet struggles considerably in specific-domain conversations. Our deeper investigation indicates that ChatGPT can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures.
  arXiv  Detail & Related papers  (2023-05-15T07:14:41Z)
• PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition [63.51569687229681]
  We argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document.
  arXiv  Detail & Related papers  (2022-12-21T04:03:33Z)
• TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters [57.59286394188025]
  We propose a novel framework for topic taxonomy completion, named TaxoCom. TaxoCom discovers novel sub-topic clusters of terms and documents. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage.
  arXiv  Detail & Related papers  (2022-01-18T07:07:38Z)
• Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation [8.688675709130289]
  RST-style discourse parsing plays a vital role in many NLP tasks. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-12T10:16:45Z)
• An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations [24.986030179701405]
  We exploit robust representations derived from multiple levels of granularity across syntax and semantics. We incorporate such representations in an end-to-end encoder-decoder neural architecture for more resourceful discourse processing.
  arXiv  Detail & Related papers  (2020-12-21T08:01:04Z)
• Multilevel Text Alignment with Cross-Document Attention [59.76351805607481]
  Existing alignment methods operate at a single, predefined level. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component.
  arXiv  Detail & Related papers  (2020-10-03T02:52:28Z)
• A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure [27.927104697483934]
  We propose a top-down neural architecture toward text-level DRS parsing. We cast discourse parsing as a split point ranking task, where a split point is classified to different levels according to its rank. In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack.
  arXiv  Detail & Related papers  (2020-05-06T09:27:20Z)

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.