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Get To The Point: Summarization with Pointer Generator Networks

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Figure 2: Baseline sequence-to-sequence model with attention. The model may attend to relevant wordsin the source text to generate novel words, e.g., to produce the novel word beat in the abstractive summaryGermany beat Argentina 2-0 the model may attend to the words victorious and win in the source text.
Figure 3: Pointer-generator model. For each decoder timestep a generation probability pcalculated, which weights the probability offrom the source text
Table 1: ROUGE F1abstractive, while those in the bottom half are extractive. Those marked with * were trained and evaluatedon the anonymized dataset, and so are not strictly comparable to our results on the original text
Figure 4: Coverage eliminates undesirable repe-tition. Summaries from our non-coverage modelcontain many duplicated n-grams while our cover-age model produces a similar number as the ref-erence summaries.
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