Abstract
The proposed model unites the robustness of the extractive and abstractive summarization strategies. Three tasks indispensable to automatic summarization, namely, apprehension, extraction, and abstraction, are performed by two specially designed networks, the highlighter RNN and the generator RNN. While the highlighter RNN collectively performs the task of highlighting and extraction for identifying the salient facts in the input text, the generator RNN fabricates the summary based on those facts. The summary is generated using word-level extraction with the help of term-frequency inverse document frequency (TFIDF) ranking factor. The union of the two strategies proves to surpass the ROUGE score results on the Gigaword dataset as compared to the simple abstractive approach for summarization.
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Lal, D.M., Singh, K.P., Tiwary, U.S. (2020). Highlighted Word Encoding for Abstractive Text Summarization. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_7
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DOI: https://doi.org/10.1007/978-3-030-44689-5_7
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