Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)
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| Challenge: | Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. |
| Approach: | They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them. |
| Outcome: | The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations. |
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