Cross-Lingual Speech-to-Text Summarization

Cryptology and Network Security - Lecture Notes in Computer Science ◽

10.1007/978-3-319-98678-4_39 ◽

2018 ◽

pp. 385-395 ◽

Cited By ~ 3

Author(s):

Elvys Linhares Pontes ◽

Carlos-Emiliano González-Gallardo ◽

Juan-Manuel Torres-Moreno ◽

Stéphane Huet

Keyword(s):

Text Summarization ◽

Cross Lingual

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Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization

10.26615/978-954-452-072-4_074 ◽

2021 ◽

Author(s):

Mram Kahla ◽

◽

Zijian Győző Yang ◽

Attila Novák ◽

◽

...

Keyword(s):

Text Summarization ◽

Fine Tuning ◽

Arabic Text ◽

Arabic Text Summarization ◽

Cross Lingual

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Parsing Morphologically Rich Languages: Introduction to the Special Issue

Computational Linguistics ◽

10.1162/coli_a_00133 ◽

2013 ◽

Vol 39 (1) ◽

pp. 15-22 ◽

Cited By ~ 25

Author(s):

Reut Tsarfaty ◽

Djamé Seddah ◽

Sandra Kübler ◽

Joakim Nivre

Keyword(s):

Natural Language ◽

Language Processing ◽

Word Order ◽

Text Summarization ◽

Special Issue ◽

Compositional Semantics ◽

Abstract Representation ◽

Word Structure ◽

Morphologically Rich Languages ◽

Cross Lingual

Parsing is a key task in natural language processing. It involves predicting, for each natural language sentence, an abstract representation of the grammatical entities in the sentence and the relations between these entities. This representation provides an interface to compositional semantics and to the notions of “who did what to whom.” The last two decades have seen great advances in parsing English, leading to major leaps also in the performance of applications that use parsers as part of their backbone, such as systems for information extraction, sentiment analysis, text summarization, and machine translation. Attempts to replicate the success of parsing English for other languages have often yielded unsatisfactory results. In particular, parsing languages with complex word structure and flexible word order has been shown to require non-trivial adaptation. This special issue reports on methods that successfully address the challenges involved in parsing a range of morphologically rich languages (MRLs). This introduction characterizes MRLs, describes the challenges in parsing MRLs, and outlines the contributions of the articles in the special issue. These contributions present up-to-date research efforts that address parsing in varied, cross-lingual settings. They show that parsing MRLs addresses challenges that transcend particular representational and algorithmic choices.

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Cross-Lingual Korean Speech-to-Text Summarization

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-14799-0_17 ◽

2019 ◽

pp. 198-206

Author(s):

HyoJeon Yoon ◽

Dinh Tuyen Hoang ◽

Ngoc Thanh Nguyen ◽

Dosam Hwang

Keyword(s):

Text Summarization ◽

Cross Lingual

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Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization

Trends and Applications of Text Summarization Techniques - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-9373-7.ch001 ◽

2020 ◽

pp. 1-31

Author(s):

Luca Cagliero ◽

Paolo Garza ◽

Moreno La Quatra

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Research Community ◽

Text Summarization ◽

Web Based ◽

Recent Advances ◽

Pros And Cons ◽

Cross Lingual

The recent advances in multimedia and web-based applications have eased the accessibility to large collections of textual documents. To automate the process of document analysis, the research community has put relevant efforts into extracting short summaries of the document content. However, most of the early proposed summarization methods were tailored to English-written textual corpora or to collections of documents all written in the same language. More recently, the joint efforts of the machine learning and the natural language processing communities have produced more portable and flexible solutions, which can be applied to documents written in different languages. This chapter first overviews the most relevant language-specific summarization algorithms. Then, it presents the most recent advances in multi- and cross-lingual text summarization. The chapter classifies the presented methodology, highlights the main pros and cons, and discusses the perspectives of the extension of the current research towards cross-lingual summarization systems.

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