Many applications of text generation such as summarization benefit from
accurately controlling the text length. Existing approaches on
length-controlled summarization either result in degraded performance or can
only control the length approximately. In this work, we present a framework to
generate summaries with precisely the specified number of tokens or sentences,
while maintaining or even improving the text quality. In addition, we jointly
train the models to predict the lengths, so our model can generate summaries
with optimal length. We evaluate the proposed framework on the CNNDM dataset
and show improved performance compared to existing methods.

该论文提出了一种能够精确控制文本长度的文本生成框架，可以生成指定数量的词语或句子的摘要，且在维持或提高文本质量的同时，通过联合训练模型预测长度，实现生成最佳长度的摘要。作者对该框架在 CNNDM 数据集上进行了评估，结果表明其性能优于现有方法。

精准长度控制摘要

Summarization with Precise Length Control

Video dubbing aims to translate the original speech in a film or television
program into the speech in a target language, which can be achieved with a
cascaded system consisting of speech recognition, machine translation and
speech synthesis. To ensure the translated speech to be well aligned with the
corresponding video, the length/duration of the translated speech should be as
close as possible to that of the original speech, which requires strict length
control. Previous works usually control the number of words or characters
generated by the machine translation model to be similar to the source
sentence, without considering the isochronicity of speech as the speech
duration of words/characters in different languages varies. In this paper, we
propose a machine translation system tailored for the task of video dubbing,
which directly considers the speech duration of each token in translation, to
match the length of source and target speech. Specifically, we control the
speech length of generated sentence by guiding the prediction of each word with
the duration information, including the speech duration of itself as well as
how much duration is left for the remaining words. We design experiments on
four language directions (German -> English, Spanish -> English, Chinese <->
English), and the results show that the proposed method achieves better length
control ability on the generated speech than baseline methods. To make up the
lack of real-world datasets, we also construct a real-world test set collected
from films to provide comprehensive evaluations on the video dubbing task.

该研究针对影视配音中原始语音和目标语音的对齐问题，提出一种机器翻译系统，通过考虑语音时长控制翻译长度，构建了真实世界情景下的测试集，实现了对自然长度的更好控制。

VideoDubber: 视频配音的语音感知长度控制机器翻译

VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing

To participate in the Isometric Spoken Language Translation Task of the IWSLT
2022 evaluation, constrained condition, AppTek developed neural
Transformer-based systems for English-to-German with various mechanisms of
length control, ranging from source-side and target-side pseudo-tokens to
encoding of remaining length in characters that replaces positional encoding.
We further increased translation length compliance by sentence-level selection
of length-compliant hypotheses from different system variants, as well as
rescoring of N-best candidates from a single system. Length-compliant
back-translated and forward-translated synthetic data, as well as other
parallel data variants derived from the original MuST-C training corpus were
important for a good quality/desired length trade-off. Our experimental results
show that length compliance levels above 90% can be reached while minimizing
losses in MT quality as measured in BERT and BLEU scores.

AppTek 使用基于神经 Transformer 的系统对英语到德语的音–语翻译任务进行了改进，通过使用源端伪标记、目标端伪标记和使用剩余字符长度编码来实现长度控制，同时使用由 MuST-C 原始语料库派生的合成数据，使翻译满足长度条件，研究结果表明，可以在最小化 BERT 和 BLEU 分数损失的同时实现超过 90％的长度符合度。