A more abstractive summarization model

• URL: http://arxiv.org/abs/2002.10959v1
• Date: Tue, 25 Feb 2020 15:22:23 GMT
• Title: A more abstractive summarization model
• Authors: Satyaki Chakraborty, Xinya Li, Sayak Chakraborty
• Abstract summary: We investigate why pointer-generator network is unable to generate novel words. We then address that by adding an Out-of-vocabulary penalty. We also report rouge scores of our model since most summarization models are evaluated with R-1, R-2, R-L scores.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pointer-generator network is an extremely popular method of text summarization. More recent works in this domain still build on top of the baseline pointer generator by augmenting a content selection phase, or by decomposing the decoder into a contextual network and a language model. However, all such models that are based on the pointer-generator base architecture cannot generate novel words in the summary and mostly copy words from the source text. In our work, we first thoroughly investigate why the pointer-generator network is unable to generate novel words, and then address that by adding an Out-of-vocabulary (OOV) penalty. This enables us to improve the amount of novelty/abstraction significantly. We use normalized n-gram novelty scores as a metric for determining the level of abstraction. Moreover, we also report rouge scores of our model since most summarization models are evaluated with R-1, R-2, R-L scores.

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