Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Abstractive Text Summarizer for Telugu Language   Abstractive Text Summarization is the process of constructing semantically
relevant shorter sentences which captures the essence of the overall meaning of
the source text. It is actually difficult and very time consuming for humans to
summarize manually large documents of text. Much of work in abstractive text
summarization is being done in English and almost no significant work has been
reported in Telugu abstractive text summarization. So, we would like to propose
an abstractive text summarization approach for Telugu language using Deep
learning. In this paper we are proposing an abstractive text summarization Deep
learning model for Telugu language. The proposed architecture is based on
encoder-decoder sequential models with attention mechanism. We have applied
this model on manually created dataset to generate a one sentence summary of
the source text and have got good results measured qualitatively.

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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: math