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In this paper we consider the problem of analysing sentence-level discourse struc- ture. We introduce discourse chunking (i.e., the identification of intra-sentential nucleus and satellite spans) as an al- ternative …...

In Statistics-Based Summarization - Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (K&M) present a noisy-channel model for sen- tence compression. The main difficulty in using this …...

We present a model for sentence com- pression that uses a discriminative large- margin learning framework coupled with a novel feature set defined on compressed bigrams as well as deep …...

Sentence compression is the task of pro- ducing a summary at the sentence level. This paper focuses on three aspects of this task which have not received de- tailed treatment …...

Sentence compression holds promise for many applications ranging from summarisa- tion to subtitle generation and subtitle gen- eration. The task is typically performed on isolated sentences without taking the sur- …...

In this paper we generalise the sen- tence compression task. Rather than sim- ply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using …...

AbstractThis paper presents two corpora produced within the RPM2 project: a multi-document summarization corpus and a sentence compression corpus. Both corpora are in French. The first one is the only …...

We present a new method that compresses sentences by removing words. In a first stage, it generates candidate compressions by re- moving branches from the source sentence’s dependency tree using …...

Previous work has shown that high quality phrasal paraphrases can be extracted from bilingual parallel corpora. However, it is not clear whether bitexts are an appropriate re- source for extracting …...

Sentence compression is important in a wide range of applications in natural language process- ing. Previous approaches of Japanese sentence compression can be divided into two groups. Word-based methods extract …...

This paper applies sentence compression models for the task of query-focused multi-document summarization in order to investigate if sentence compression improves the overall summariza- tion performance. Both compression and summarization …...

We consider the problem of using sentence compression techniques to facilitate query- focused multi-document summarization. We present a sentence-compression-based frame- work for the task, and design a series of learning-based …...

Joint compression and summarization has been used recently to generate high quality summaries. However, such word-based joint optimization is computationally expensive. In this paper we adopt the ‘sentence compression + …...

Sentence compression techniques often assemble output sentences using frag- ments of lexical sequences such as n- grams or units of syntactic structure such as edges from a dependency tree repre- …...

ive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain Jackie Chi Kit Cheung University of Toronto 10 King’s College Rd., Room 3302 Toronto, ON, Canada M5S 3G4 jcheung@cs.toronto.edu Gerald …...

Sentence compression has been shown to benefit from joint inference involving both n-gram and dependency-factored objec- tives but this typically requires expensive integer programming. We explore instead the use of …...

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summariza- tion. We propose an innovative sentence compression method by considering every node in …...

In this paper we introduce a novel single-document summarization method based on a hidden semi-Markov model. This model can naturally model single-document summarization as the optimization problem of selecting the …...

We describe two systems for text simplification using typed dependency structures, one that performs lexical and syntactic simplification, and another that performs sentence compression optimised to satisfy global text constraints …...

We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of- speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that …...

AbstractSentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual …...

AbstractSentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase …...

AbstractIn 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 …...

AbstractSearch applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence …...

We have seen significant improvements in machine translation due to the usage of deep learn- ing. While the improvements in translation quality are impressive, the encoder-decoder archi- tecture enables many …...

AbstractNon-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind their autoregressive counterparts. In this …...

AbstractSentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, …...

AbstractSentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized …...

AbstractAlthough the fluency of automatically generated abstractive summaries has improved significantly with advanced methods, the inconsistency that remains in summarization is recognized as an issue to be addressed. In this …...