| Challenge: | Existing techniques for constrained compression are slow and require third-party solvers. |
| Approach: | They propose a query-focused sentence compression technique which constructs length and lexically constrained compressions in linear time by growing a subgraph in the dependency parse of a sentence. |
| Outcome: | The proposed technique achieves an 11x empirical speedup over baseline methods while improving query-focused applications. |
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Cross-Lingual Sentence Compression for Length-Constrained Subtitles in Low-Resource Settings (2025.coling-main)
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| Challenge: | a new system for sentence compression is developed for broadcast and live media . the goal is to reduce the on-screen visual constraints of the text . |
| Approach: | They develop a machine translation and sentence compression system that trains on openly available parallel corpora organized by compression ratios. |
| Outcome: | The proposed system preserves high semantic meaning and metric evaluations for compressed contexts. |
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning (2022.acl-long)
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| Challenge: | Recent unsupervised sentence compression approaches use custom objectives to guide discrete search, but guided search is expensive at inference time. |
| Approach: | They propose to use reinforcement learning to train effective sentence compression models that are also fast when generating predictions. |
| Outcome: | The proposed model outperforms other unsupervised models while being faster at inference time. |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models (2024.findings-acl)
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| Challenge: | Existing sentences do not consider the length constraints in extractive summarization because of their limited model abilities. |
| Approach: | They propose an approach that incorporates length constraints without model modifications into sentences . they use traditional sentence compression datasets to transform them into instruction format . |
| Outcome: | The proposed method can consider the length constraint through instructions without model modifications. |
Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)
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| Challenge: | Existing methods for decoding dependency trees are 10 times faster than current ones. |
| Approach: | They propose a graph-based method to tackle a dependency tree linearization task . they propose to solve a Traveling Salesman Problem and combine the solution into a projective tree . |
| Outcome: | The proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding. |
CacheNotes: Task-Aware Key-Value Cache Compression for Reasoning-Intensive Knowledge Tasks (2026.eacl-long)
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| Challenge: | Current methods for integrating external knowledge into Large Language Models (LLMs) face limitations with broad, multi-source queries, while long-context models are computationally prohibitive. |
| Approach: | They propose a task-aware key-value cache compression method that generates a sequence of CPTs from a corpus and guides a one-time compression of the corpus into a compact, reusable KV cache. |
| Outcome: | The proposed method outperforms Retrieval-Augmented Generation (RAG) on Question-Answering tasks and reduces latency by over 4. |
Extending Context Window of Large Language Models via Semantic Compression (2024.findings-acl)
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| Challenge: | Existing models rely on a quadratic computation to generate long texts . current models impose limitations on the length of text inputs . |
| Approach: | They propose a semantic compression method that extends the context window of large language models . the method reduces the semantic redundancy of long inputs before passing them to the LLMs . |
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With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms (2021.eacl-main)
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| Challenge: | Sentence Compression is the task of generating a shorter, yet grammatical, version of a given sentence, preserving the essence of the original sentence. |
| Approach: | They propose a Black-Box Optimizer for Compression to find the best candidates for compression . they use a black-box compression algorithm to predict how well each sentence could be compressed . |
| Outcome: | The proposed algorithm improves both accuracy and Rouge-F1-score on three datasets. |
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning (2026.findings-acl)
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| Challenge: | Experiments show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
| Approach: | They propose a universal, lightweight compressor that distills relevant evidence from retrieved documents into a concise summary for seamless integration into in-context RAG. |
| Outcome: | Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
More Discriminative Sentence Embeddings via Semantic Graph Smoothing (2024.eacl-short)
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| Challenge: | Text categorization is a natural language processing task that involves arranging texts into coherent groups based on their content. |
| Approach: | They propose to use semantic graph smoothing to enhance sentence embeddings from pretrained models to improve results for supervised and unsupervised document categorization tasks. |
| Outcome: | The proposed method improves sentences embeddings for supervised and unsupervised document categorization tasks. |