Challenge: Compared with sentence-level topic structure, paragraph-level topics can grasp and understand the context of a document from a higher level.
Approach: They propose a hierarchical paragraph-level topic structure representation with three layers to guide corpus construction.
Outcome: The proposed method achieves the largest Chinese paragraph-level topic structure corpus, achieving high quality.

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From Documents to Segments: A Contextual Reformulation for Topic Assignment (2026.findings-acl)

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Challenge: Traditional topic modeling treats each document as a single, coherent unit of topic.
Approach: They propose a paradigm that redefines topic assignment at the level of segments . they propose 'segment intrusion task' to extend word intrusion to the span level .
Outcome: The proposed paradigm improves topic purity, interpretability and applicability to multi-theme corpora.
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)

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Challenge: Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.
Approach: They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document.
Outcome: The proposed method improves extractive summarization performance on CNN/Daily Mail dataset.
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)

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Challenge: Existing models focus on local word prediction, and cannot make high level plans on what to generate.
Approach: They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment.
Outcome: The proposed system produces long texts with significantly better quality and faster convergence speed.
MCDTB: A Macro-level Chinese Discourse TreeBank (C18-1)

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Challenge: Discourse analysis is becoming increasingly important in the field of natural language processing.
Approach: They propose to annotate macro discourse information and additional discourse information to make annotation more objective and accurate.
Outcome: The results show that the annotations are more objective and accurate than the previous ones.
Discourse Representation Parsing for Sentences and Documents (P19-1)

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Challenge: Experimental results show that our model outperforms competitive baselines by a wide margin.
Approach: They propose a neural model which parses discourse structures of arbitrary length and granularity.
Outcome: The proposed model outperforms baseline models on sentence- and document-level benchmarks.
Exploring Discourse Structure in Document-level Machine Translation (2023.emnlp-main)

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Challenge: Existing methods for document-level machine translation (DocMT) are under-utilizing the context.
Approach: They propose a paragraph-to-paragraph translation mode that utilizes discourse information . they propose 'speech-based' translation mode which utilizes contextual information based on the context .
Outcome: The proposed method utilizes discourse information and performs better than previous methods.
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)

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Challenge: Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment.
Approach: They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies.
Outcome: The proposed model can be used to augment existing PLM-based models and improve their performance on Chinese LLaMA and Alpaca datasets.
EDTC: A Corpus for Discourse-Level Topic Chain Parsing (2021.findings-emnlp)

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Challenge: Discourse analysis is a fundamental part of natural language processing.
Approach: They propose a discourse-level topic chain parsing system which can be automated . they propose lexical cohesion modeling instead of lexically measuring topic structure .
Outcome: The proposed system is robust and reliable, and can provide high reliability and low confidence scores.
Inducing Document Structure for Aspect-based Summarization (P19-1)

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Challenge: Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document.
Approach: They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective .
Outcome: The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries.
Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)

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Challenge: Existing methods for generating readable outlines are inability to segment long texts .
Approach: They propose an unsupervised framework to guide large language model outline generation . framework ensures each structured plot encapsulates complete causality by accurately identifying plot boundaries.
Outcome: The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries.

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