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Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic text summarization in different languages using various text summarization approaches. The focal centre of this survey paper is to present the research done on text summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of text summarization approaches applied on both Indian and non-Indian languages. [ABSTRACT FROM AUTHOR]

According to the problem that the model in the generative text summarization doesn’t fully understand the semantics of the text and the generated summary lacks key information, this paper proposed a Key-BERT-Pen model that integrated keywords in Chinese abstract generation. The KBPM first used the TextRank method to extract the keywords in the text, and then obtained a more accurate contextual representation through the BERT pre-training model using the extracted keywords and the original text. Finally, it input the obtained word vector into a pointer model with a dual attention mechanism, and the pointer model took vocabularies from the vocabulary or original text to generate the final abstract. Experimental results show that the KBPM can generate text summaries with better readability and higher ROUGE scores. The comparative analysis also verifies that the KBPM effectively solves the problem of the lack of key information in the generated abstract. [ABSTRACT FROM AUTHOR]