With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms
Yotam Shichel, Meir Kalech, Oren Tsur

TL;DR
This paper introduces B-BOC, a black-box optimizer for sentence compression that selects optimal sentences or sequences to maximize compression quality and rate, outperforming existing methods.
Contribution
The paper presents a novel black-box optimization framework for sentence compression that effectively predicts and assigns optimal compression ratios, improving performance over existing algorithms.
Findings
B-BOC improves compression accuracy.
B-BOC increases Rouge-F1-score.
Effective for both single-sentence and sequence compression.
Abstract
Sentence Compression is the task of generating a shorter, yet grammatical version of a given sentence, preserving the essence of the original sentence. This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed -- find the best candidates for compression in order to maximize both compression rate and quality. Given a required compression ratio, we consider two scenarios: (i) single-sentence compression, and (ii) sentences-sequence compression. In the first scenario, our optimizer is trained to predict how well each sentence could be compressed while meeting the specified ratio requirement. In the latter, the desired compression ratio is applied to a sequence of sentences (e.g., a paragraph) as a whole, rather than on each individual sentence. To achieve that, we use B-BOC to assign an…
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