In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences. To remove the need for paired corpora, we emulate a summarization task and add noise to extend sentences and train a denoising auto-encoder to recover the original, constructing an end-to-end training regime without the need for any examples of compressed sentences. We conduct a human evaluation of our model on a standard text summarization dataset and show that it performs comparably to a supervised baseline based on grammatical correctness and retention of meaning. Despite being exposed to no target data, our unsupervised models learn to generate imperfect but reasonably readable sentence summaries. Although we underperform supervised models based on ROUGE scores, our models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.
Early sentence compression approaches were extractive, focusing on deletion of uninformative words from sentences through learned rules @cite_32 or linguistically-motivated heuristics @cite_3 . The first abstractive approaches also relied on learned syntactic transformations @cite_22 .
@cite_22 @cite_32 @cite_3
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We present a new method that compresses sentences by removing words. In a first stage, it generates candidate compressions by removing branches from the source sentence's dependency tree using a Maximum Entropy classifier. In a second stage, it chooses the best among the candidate compressions using a Support Vector Machine Regression model. Experimental results show that our method achieves state-of-the-art performance without requiring any manually written rules. In this article we generalize the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present an experimental study showing that humans can naturally create abstractive sentences using a variety of rewrite operations, not just deletion. We next create a new corpus that is suited to the abstractive compression task and formulate a discriminative tree-to-tree transduction model that can account for structural and lexical mismatches. The model incorporates a grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression-specific loss functions. Sentence compression techniques often assemble output sentences using fragments of lexical sequences such as ngrams or units of syntactic structure such as edges from a dependency tree representation. We present a novel approach for discriminative sentence compression that unifies these notions and jointly produces sequential and syntactic representations for output text, leveraging a compact integer linear programming formulation to maintain structural integrity. Our supervised models permit rich features over heterogeneous linguistic structures and generalize over previous state-of-theart approaches. Experiments on corpora featuring human-generated compressions demonstrate a 13-15 relative gain in 4gram accuracy over a well-studied language model-based compression system. We introduce a manually-created, multireference dataset for abstractive sentence and short paragraph compression. First, we examine the impact of singleand multi-sentence level editing operations on human compression quality as found in this corpus. We observe that substitution and rephrasing operations are more meaning preserving than other operations, and that compressing in context improves quality. Second, we systematically explore the correlations between automatic evaluation metrics and human judgments of meaning preservation and grammaticality in the compression task, and analyze the impact of the linguistic units used and precision versus recall measures on the quality of the metrics. Multi-reference evaluation metrics are shown to offer significant advantage over single reference-based metrics.