Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

Li, Chen; Liu, Yang; Liu, Fei; Zhao, Lin; Weng, Fuliang

doi:10.3115/v1/d14-1076

Cited by 46 publications

(33 citation statements)

References 21 publications

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“…For multi-document summarization, extraction and redundancy/compression of sentences have been modeled by integer linear programming and approximation algorithms [23,13,3,1,18,4,35]. Supervised and semi-supervised learning based extractive summarization was studied in [34].…”

Section: Related Workmentioning

confidence: 99%

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

Verma¹,

Lee²

2018

CyS

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Abstract. Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.

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Section: Related Workmentioning

confidence: 99%

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

Verma¹,

Lee²

2018

CyS

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“…We also find Farzindar (2014), focusing on how summarization tasks can improve social media retrieval and event detection, and Li et al (2014), in which the multi-document summarization by sentence compression is explored.…”

Section: Related Workmentioning

confidence: 99%

A novel concept-level approach for ultra-concise opinion summarization

Lloret

Boldrini

Vodolazova

et al. 2015

Expert Systems with Applications

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The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users comunicate, exchange, and share opinions. The explotation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed.Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of stateof-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

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“…Li et al (2013) propose a guided sentence compression model with ILP-based summary sentence selection. Their following work (Li et al, 2014) incorporate various constraints on constituent parse trees to improve the linguistic quality of the compressed sentences. In these studies, the bestperforming systems require supervised learning for different subtasks.…”

Section: Related Workmentioning

confidence: 99%

Phrase-based Compressive Cross-Language Summarization

Yao¹,

Wan

Xiao³

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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The task of cross-language document summarization is to create a summary in a target language from documents in a different source language. Previous methods only involve direct extraction of automatically translated sentences from the original documents. Inspired by phrasebased machine translation, we propose a phrase-based model to simultaneously perform sentence scoring, extraction and compression. We design a greedy algorithm to approximately optimize the score function. Experimental results show that our methods outperform the state-of-theart extractive systems while maintaining similar grammatical quality.

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Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

Cited by 46 publications

References 21 publications

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

A novel concept-level approach for ultra-concise opinion summarization

Phrase-based Compressive Cross-Language Summarization

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