# With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms

arXiv: https://arxiv.org/abs/2101.10096 · HTML: https://mlsanity.com/paper/2101.10096/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Learning To Split and Rephrase From Wikipedia Edit History](https://mlsanity.com/paper/1808.09468/) (2018)
- [Improving sentence compression by learning to predict gaze](https://mlsanity.com/paper/1604.03357/) (2016)
- [Split and Rephrase](https://mlsanity.com/paper/1707.06971/) (2017)
- [Simple Unsupervised Summarization by Contextual Matching](https://mlsanity.com/paper/1907.13337/) (2019)
- [Split and Rephrase: Better Evaluation and a Stronger Baseline](https://mlsanity.com/paper/1805.01035/) (2018)
- [A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization](https://mlsanity.com/paper/1606.07548/) (2016)
- [Sentence Compression as Tree Transduction](https://mlsanity.com/paper/1401.5693/) (2014)

## Built on by (most cited first, 2 of 2)

- [Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI](https://mlsanity.com/paper/2309.12444/) (2023)
- [From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences](https://mlsanity.com/paper/2312.05172/) (2023)

## Abstract (arXiv)

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 optimal compression ratio to each sentence, then cast it as a Knapsack problem, which we solve using bounded dynamic programming. We evaluate B-BOC on both scenarios on three datasets, demonstrating that our optimizer improves both accuracy and Rouge-F1-score compared to direct application of other compression algorithms.