| Challenge: | Sentence splitting is a key component of sentence simplification and has been shown to help human comprehension. |
| Approach: | They propose to use a discourse connective to generate a sentence that is shorter than the input text. |
| Outcome: | The proposed models outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical. |
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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. |
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
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CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations (2025.findings-acl)
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| Challenge: | Discourse parsing datasets based on conversations are restricted to a single domain . a lack of discourse structures in audio-based conversations is a challenge . |
| Approach: | They introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations. |
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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. |
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Semantically Driven Sentence Fusion: Modeling and Evaluation (2020.findings-emnlp)
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| Challenge: | Sentence fusion is the task of joining related sentences into coherent text. |
| Approach: | They propose a method where ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. |
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A Language Model-based Generative Classifier for Sentence-level Discourse Parsing (2021.emnlp-main)
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| Challenge: | Existing methods to consider textual coherence are limited in labeled data. |
| Approach: | They propose a language model-based generative classifier that uses labels as input and embeds labels into their representations. |
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DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion (N19-1)
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| Challenge: | Existing datasets for sentence fusion are small and insufficient for training modern neural models. |
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Text Segmentation by Cross Segment Attention (2020.emnlp-main)
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| Challenge: | Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents. |
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Simple and Effective Text Simplification Using Semantic and Neural Methods (P18-1)
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| Challenge: | Sentence splitting is a major simplification operation. |
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Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
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