These requirements enable us to represent sentence compression as the extraction of subtrees from a sentence.

Since KNP internally has a flag that indicates either an “obligatory case” or an “adj acent case”, we regarded dependency relations flagged by KNP as obligatory in the sentence compression .

Text summarization is often addressed as a task of simultaneously performing sentence extraction and sentence compression (Berg-Kirkpatrick et al., 2011; Martins and Smith, 2009).

We will formalize the unified task of sentence compression and extraction as a budgeted monotone nondecreasing submodular function maximization with a cost function.

In this paper, we address the task of summarization of Japanese text by means of sentence compression and extraction.

Therefore, sentence compression can be represented as edge pruning.

(2011) formulated a unified task of sentence extraction and sentence compression as an ILP.

We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization.

Sentence compression techniques (Knight and Marcu, 2000; Clarke and Lapata, 2008) are the standard for producing a compact and grammatical version of a sentence while preserving relevance, and prior research (e.g.

Similarly, strides have been made to incorporate sentence compression into query-focused MDS systems (Zajic et al., 2006).

Most attempts, however, fail to produce better results than those of the best systems built on pure extraction-based approaches that use no sentence compression .

Our work is more related to the less studied area of sentence compression as applied to (single) document summarization.

We now present our query-focused MDS framework consisting of three steps: Sentence Ranking, Sentence Compression and Postprocessing.

In addition, we propose a multitask learning framework to take advantage of existing data for extractive summarization and sentence compression .

similar manner as described in §2, but with an additional component for the sentence compressor , and slight modifications in the other components.

In addition, we included hard constraints to prevent the deletion of certain arcs, following previous work in sentence compression (Clarke and Lapata, 2008).

(2011), but we augmented the training data with extractive summarization and sentence compression datasets, to help train the

For sentence compression , we adapted the Simple English Wikipedia dataset of Woodsend and Lapata (2011), containing aligned sentences for 15,000 articles from the English and Simple English Wikipedias.

However, extending these models to allow for sentence compression (as will be detailed in §3) breaks the diminishing returns property, making submodular optimization no longer applicable.

For example, such solvers are unable to take advantage of efficient dynamic programming routines for sentence compression (McDonald, 2006).

0 We propose multitask learning (§4) as a principled way to train compressive summarizers, using auxiliary data for extractive summarization and sentence compression .

The goal is to take advantage of existing data for related tasks, such as extractive summarization (task #2), and sentence compression (task #3).

0 For the sentence compression task, the parts correspond to arc-deletion features only.

We address this challenge with contributions in two folds: first, we introduce the new task of image caption generalization, formulated as visually-guided sentence compression , and present an efficient algorithm based on dynamic beam search with dependency-based constraints.

In comparison to prior work on sentence compression , our approach falls somewhere between unsupervised to distant-supervised approach (e. g., Turner and Charniak (2005), Filippova and Strube (2008)) in that there is not an in-domain training corpus to learn generalization patterns directly.

Casting the generalization task as visually-guided sentence compression with lightweight revisions, we formulate a constraint optimization problem that aims to maximize content selection and local linguistic fluency while satisfying constraints driven from dependency parse trees.