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Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget

Summary

Researchers propose 'Telegraph English,' a readable symbolic format for compressing context in multi-hop question answering. It outperforms matched-budget baselines, including coherent summaries, by 13-20 F1 points. The advantage does not increase with reasoning depth.

SourcearXiv Computational LinguisticsAuthor: Sisong Bei, Mikhail L. Arbuzov, Ziwei Dong, Dmitri Kalaev, Alexey Shvets
Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget
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[2606.14875] Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget

[Submitted on 12 Jun 2026]

Title:Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget

View a PDF of the paper titled Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget, by Sisong Bei and 4 other authors

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Abstract:We study context compression for multi-hop question answering with small language models. We propose Telegraph English, a readable symbolic format that rewrites retrieved passages into structured entity-relation statements, preserving reasoning evidence at lower token cost. In controlled experiments on MuSiQue, TwoWiki, and HotpotQA, Telegraph English outperforms three matched-budget compression baselines (character-level deletion, truncation, and random sub-sampling) on every dataset, with gains of 13 to 20 F1 percentage point. It also outperforms a coherent prose summary produced by the same encoder on the hardest dataset. A pre-registered depth-interaction hypothesis is null: the advantage does not grow with reasoning depth within datasets. We interpret these results as evidence that readable symbolic re-expression preserves entity content more densely than either natural language or coherent summarization at matched token budget.

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Computation and Language (cs.CL)

Cite as: arXiv:2606.14875 [cs.CL]

(or arXiv:2606.14875v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.14875

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Submission history

From: Alexey Shvets [view email] [v1] Fri, 12 Jun 2026 18:27:23 UTC (67 KB)

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Key points and analysis

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Key points

  • Telegraph English converts retrieved passages into structured entity-relation statements, preserving evidence efficiently.
  • In experiments on MuSiQue, TwoWiki, and HotpotQA, it outperformed deletion, truncation, and sub-sampling baselines by 13-20 F1 points.
  • It also outperformed coherent prose summaries on the hardest dataset. The depth-interaction hypothesis was not supported.

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