Learning bilingual word embedding for automatic text summarization in low resource language.

Studies in low-resource languages have become more challenging with the increasing volume of texts in today's digital era. Also, the lack of labeled data and text processing libraries in a language further widens the research gap between high and low-resource languages, such as English and Indonesian. This has led to the use of a transfer learning approach, which applies pre-trained models to solve similar problems, even in different languages by using bilingual or cross-lingual word embedding. Therefore, this study aims to investigate two bilingual word embedding methods, namely VecMap and BiVec, for Indonesian – English language and evaluates them for bilingual lexicon induction and text summarization tasks. The generated bilingual embedding was compared with MUSE (Multilingual Unsupervised and Supervised Embeddings) as the existing multilingual word created with the generative adversarial network method. Furthermore, the VecMap was improved by creating shared vocabulary spaces and mapping the unshared ones between languages. The result showed the embedding produced by the joint methods of BiVec, performed better in intrinsic evaluation, especially with CSLS (Cross-Domain Similarity Local Scaling) retrieval. Meanwhile, the improved VecMap outperformed the regular type by 16.6% without surpassing the BiVec evaluation score. These methods enabled model transfer between languages when applied to cross-lingual-based text summarization. Moreover, the ROUGE score outperformed classical text summarization by adding only 10% of the training dataset of the target language. [ABSTRACT FROM AUTHOR]