Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing

Zhang, Jiajun; Zhou, Yu; Zong, Chengqing

doi:10.1109/taslp.2016.2586608

Cited by 61 publications

(48 citation statements)

References 13 publications

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“…Enhanced Predicate Argument Structure Fusing [9]. 2016 Zhang, Jiajun Zhou, Yu Zong, Chengqing [9] Integer linear optimization [9].…”

Section: Cross-language Summarization Via Translation Modelmentioning

confidence: 99%

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Application of Different Algorithms to Optimize Abstractive Summarization

Bhumika*,

Srikanth

2020

IJRTE

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Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithms for abstractive summarization then on the basis of merits of various algorithms we discuss, how various algorithm may help to optimize Abstractive Summarization.

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“…Enhanced Predicate Argument Structure Fusing [9]. 2016 Zhang, Jiajun Zhou, Yu Zong, Chengqing [9] Integer linear optimization [9].…”

Section: Cross-language Summarization Via Translation Modelmentioning

confidence: 99%

“…Predicate Argument Structure and Fusing. [9]In cross language summarization we can generate summary in the targeted language by merging multiple bilingual PAS structures using integer linear optimization that attempts to maximize the salience score and the translation quality simultaneously.…”

Section: Cross-language Summarization Via Translation Modelmentioning

confidence: 99%

Application of Different Algorithms to Optimize Abstractive Summarization

Bhumika*,

Srikanth

2020

IJRTE

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“…Recent methods improved the quality of cross-lingual summarization using a translation quality score [23,2,25] and the information of the documents in both languages [22,26]. These methods are described in the next subsections.…”

Section: Cross-language Automatic Text Summarizationmentioning

confidence: 99%

“…Unlike Wan who generated extractive CLATS, Zhang et al analyzed Predicate-Argument Structures (PAS) to obtain an abstractive English-to-Chinese CLATS [26]. They built a pool of bilingual concepts and facts represented by the bilingual elements of the source-side PAS and their target-side counterparts from the alignment between source texts and Google Translate translations.…”

Section: Joint Analysis In Both Languagesmentioning

confidence: 99%

Cross-Language Text Summarization Using Sentence and Multi-Sentence Compression

Pontes

Huet

Torres‐Moreno

et al. 2018

Lecture Notes in Computer Science

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Abstract. Cross-Language Automatic Text Summarization produces a summary in a language different from the language of the source documents. In this paper, we propose a French-to-English cross-lingual summarization framework that analyzes the information in both languages to identify the most relevant sentences. In order to generate more informative cross-lingual summaries, we introduce the use of chunks and two compression methods at the sentence and multi-sentence levels. Experimental results on the MultiLing 2011 dataset show that our framework improves the results obtained by state-of-the art approaches according to ROUGE metrics.

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“…These scores were used to select and compress sentences simultaneously. Zhang et al used Predicate-Argument Structures (PAS) to identify a set of concepts and facts in the source side, and their counterparts in the target side with the help of an alignment method [30]. The relevance of concepts and facts are estimated using the CoRank algorithm [25], while summaries were produced by fusing the most relevant source-side PAS elements considering their translation quality.…”

Section: Related Workmentioning

confidence: 99%

Cross-Lingual Speech-to-Text Summarization

Pontes

González-Gallardo

Torres‐Moreno

et al. 2018

Cryptology and Network Security

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Cross-Lingual Text Summarization generates a summary in a language different from the language of the source documents. We propose a French-to-English cross-lingual transcript summarization framework that automatically segments a French transcript and analyzes the information in the source and the target languages to estimate the saliency of sentences. Additionally, we use a multi-sentence compression method to simultaneously compress and improve the informativeness of sentences. Experimental results show that our framework outperformed extractive methods using automatic sentence segmentation, even with transcription errors.

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Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing

Cited by 61 publications

References 13 publications

Application of Different Algorithms to Optimize Abstractive Summarization

Application of Different Algorithms to Optimize Abstractive Summarization

Cross-Language Text Summarization Using Sentence and Multi-Sentence Compression

Cross-Lingual Speech-to-Text Summarization

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