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LenAtten: An Effective Length Controlling Unit For Text Summarization
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Fixed length summarization aims at generating summaries with a preset number of words or characters. Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality. In this work, we present an effective length controlling unit Length Attention (LenAtten) to break this trade-off. Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability. In the task of generating a summary with the target length, our model is 732 times better than the best-performing length controllable summarizer in length controllability on the CNN/Daily Mail dataset.
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Controlling Summarization Length Through EOS Token Weighting
Weighting the EOS token in the loss during fine-tuning reduces too-long summaries on CNN/DailyMail and fixed-length XL-sum, but not on dynamic-length XL-sum.
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