Papers with compression models

2 papers
Sentence Compression for Arbitrary Languages via Multilingual Pivoting (D18-1)

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Challenge: a new study advocates the use of bilingual corpora for sentence compression models . previous work focused on word deletion, while others view sentence compression as a general text rewriting problem.
Approach: They propose to use bilingual corpora for training sentence compression models.
Outcome: The proposed model can be trained for any language as long as a bilingual corpus is available . it performs arbitrary rewrites without access to compression specific data .
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation (2025.findings-acl)

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Challenge: Document compression methods suffer from accuracy losses and limited context size.
Approach: They propose a method that achieves a 16x compression rate with minimal accuracy loss . they show that PISCO outperforms existing compression models by 8% in accuracy .
Outcome: The proposed method outperforms existing compression models by 8% in accuracy.

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