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


Core Concepts
Advancing Chinese text analysis through paragraph-level topic representation and corpus construction.
Abstract
This content delves into the importance of paragraph-level topic structure in Chinese texts, outlining the challenges and solutions in constructing a corpus for topic segmentation and outline generation. It discusses the hierarchical representation model, the two-stage annotation method, and the application of the corpus in downstream tasks like discourse parsing. Abstract Topic segmentation and outline generation aim to divide documents into coherent sections and generate subheadings. Paragraph-level topic structure provides a higher-level context for document understanding. Lack of large-scale Chinese paragraph-level corpora hinders research and applications. Introduction Well-written documents consist of semantically coherent segments revolving around specific topics. Paragraph-level topic structure aids in understanding the overall context of a document. English has more research on topic segmentation compared to Chinese. Chinese Paragraph-level Topic Structure Representation Proposed a hierarchical representation model with three layers for comprehensive topic structure. Subheadings and titles are used to represent richer information at the paragraph level. Chinese Paragraph-level Topic Structure Corpus Construction Data source: News documents from Xinhua News Agency in the Gigaword corpus. Two-stage annotation method: Automatic extraction followed by manual verification for high-quality corpus. Statistical details of CPTS: Average words per document, paragraphs per document, words per subheading, etc. Experiments on Corpus Evaluation Topic Segmentation Baselines: Segbot, PN-XLNet, TM-BERT, BERT+Bi-LSTM, Hier. BERT, ChatGPT. Evaluation metrics: Pk, WindowDiff, Segmentation Similarity, Boundary Similarity, and macro-F1. Results: BERT+Bi-LSTM and Hier. BERT outperform other models in topic segmentation. Outline Generation Baselines: BART, T5, ChatGPT. Evaluation metrics: ROUGE, BLEU, BertScore, and manual evaluation. Results: T5 (24) performs best among baselines in outline generation. Title Generation Baselines: BART, T5, ChatGPT. Results: T5 (24) outperforms other models in title generation. Application in Discourse Parsing Used CPTS to enhance paragraph-level discourse parsing performance. Real topic structure in CPTS improves parser performance. Discussion and Future Work Applicability of annotation method in various genres. Potential challenges in expanding joint learning framework and exploring hierarchical topic structures. Conclusion Proposed a comprehensive approach for constructing a Chinese paragraph-level topic structure corpus. Validated the corpus through experiments on topic segmentation, outline generation, and discourse parsing. Bibliographical References Includes references to relevant works in text segmentation, discourse parsing, and text summarization.
Stats
The lack of large-scale Chinese paragraph-level corpora hinders research and applications. The CPTS corpus contains about 14393 documents with high quality. The average number of words per document is 1727.96. The corpus construction method involves a two-stage annotation process.
Quotes
"Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings." "The lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained relative research and applications."

Deeper Inquiries

How can the hierarchical topic structure representation benefit other NLP tasks beyond topic segmentation and outline generation

The hierarchical topic structure representation can benefit other NLP tasks beyond topic segmentation and outline generation by providing a more comprehensive understanding of the document's content and structure. For tasks like document summarization, the hierarchical representation can help in identifying key topics and subtopics within the document, leading to more informative and concise summaries. In discourse parsing, the hierarchical structure can serve as a guide for building a coherent discourse tree by capturing the relationships between different topics and subtopics. Additionally, in information retrieval tasks, the hierarchical representation can aid in quickly locating relevant information within documents by understanding the overall topic structure. Overall, the hierarchical topic structure representation can enhance the performance of various downstream NLP tasks by providing a more nuanced understanding of the document's content organization.

What are the implications of the lack of Chinese paragraph-level corpora on the advancement of NLP research in Chinese texts

The lack of Chinese paragraph-level corpora poses significant challenges to the advancement of NLP research in Chinese texts. Without access to high-quality corpora like the Chinese Paragraph-level Topic Structure corpus (CPTS) discussed in the context, researchers face limitations in developing and evaluating models for tasks such as topic segmentation, outline generation, and discourse parsing. The absence of such corpora hinders the progress of research in understanding the higher-level topic structure of Chinese documents, which is crucial for tasks like document summarization, information retrieval, and discourse analysis. Furthermore, the lack of corpora restricts the training and evaluation of large language models on Chinese text data, impacting the development of advanced NLP applications in Chinese language processing. Addressing this gap by creating more comprehensive and high-quality corpora will be essential for driving innovation and progress in NLP research for Chinese texts.

How can the findings from this study be applied to improve the efficiency and accuracy of large language models in processing Chinese text data

The findings from this study can be applied to improve the efficiency and accuracy of large language models in processing Chinese text data by enhancing the models' understanding of the hierarchical topic structure within documents. By incorporating the hierarchical topic structure representation from the CPTS corpus into the training data of large language models, the models can learn to better capture the relationships between topics, subtopics, and paragraphs in Chinese documents. This enriched understanding can lead to more coherent and contextually relevant text generation, summarization, and information retrieval by the models. Additionally, leveraging the topic structure information can help large language models in generating more structured and organized outputs, aligning with the natural flow of topics in Chinese text. Overall, integrating the insights and annotations from the CPTS corpus can enhance the performance of large language models in processing Chinese text data, leading to more accurate and contextually relevant language understanding and generation.
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