Multimodal abstractive summarization for videos (MAS) requires generating a
concise textual summary to describe the highlights of a video according to
multimodal resources, in our case, the video content and its transcript.
Inspired by the success of the large-scale generative pre-trained language
model (GPLM) in generating high-quality textual content (e.g., summary), recent
MAS methods have proposed to adapt the GPLM to this task by equipping it with
the visual information, which is often obtained through a general-purpose
visual feature extractor. However, the generally extracted visual features may
overlook some summary-worthy visual information, which impedes model
performance. In this work, we propose a novel approach to learning the
summary-worthy visual representation that facilitates abstractive
summarization. Our method exploits the summary-worthy information from both the
cross-modal transcript data and the knowledge that distills from the pseudo
summary. Extensive experiments on three public multimodal datasets show that
our method outperforms all competing baselines. Furthermore, with the
advantages of summary-worthy visual information, our model can have a
significant improvement on small datasets or even datasets with limited
training data.

提出一种新方法，利用文本和图像数据中摘要值得特征，并采用大规模生成预训练语言模型生成视频的多模态生成式摘要，相比其他基线方法，我们的方法在三个公共数据集上的表现是最好的。

视频摘要中适合总结的视觉表现学习

Learning Summary-Worthy Visual Representation for Abstractive  Summarization in Video

Existing language models excel at writing from scratch, but many real-world
scenarios require rewriting an existing document to fit a set of constraints.
Although sentence-level rewriting has been fairly well-studied, little work has
addressed the challenge of rewriting an entire document coherently. In this
work, we introduce the task of document-level targeted content transfer and
address it in the recipe domain, with a recipe as the document and a dietary
restriction (such as vegan or dairy-free) as the targeted constraint. We
propose a novel model for this task based on the generative pre-trained
language model (GPT-2) and train on a large number of roughly-aligned recipe
pairs (this https URL).
Both automatic and human evaluations show that our model out-performs existing
methods by generating coherent and diverse rewrites that obey the constraint
while remaining close to the original document. Finally, we analyze our model's
rewrites to assess progress toward the goal of making language generation more
attuned to constraints that are substantive rather than stylistic.

本文介绍了将文档级别的有针对性的内容转移应用于配方领域中，通过生成式预训练语言模型 (GPT-2) 模型来实现，在保持原始文档的情况下，生成了遵守限制且连贯多样的改写，并评估了改写效果。同时，倡导语言生成更具有实质性的限制性。

内容优先：以文档为级别的目标内容转移

Substance over Style: Document-Level Targeted Content Transfer

We present a simple yet effective method for generating high quality
classical Chinese poetry with Generative Pre-trained Language Model (GPT). The
method adopts a simple GPT model, without using any human crafted rules or
features, or designing any additional neural components. While the proposed
model learns to generate various forms of classical Chinese poems, including
Jueju, L\"{u}shi, various Cipai and Couples, the generated poems are of very
high quality. We also propose and implement a method to fine-tune the model to
generate acrostic poetry. To the best of our knowledge, this is the first to
employ GPT in developing a poetry generation system. We have released an online
mini demonstration program on Wechat to show the generation capability of the
proposed method for classical Chinese poetry.

利用预训练语言模型生成高质量文言诗的简单有效方法。该方法采用 GPT 模型，生成各种形式的文言诗，包括绝句、律诗、词牌和对联。同时进行了微调并实现了生成藏头诗的方法。我们已在微信上发布了一个在线演示程序，以展示这种方法在生成文言诗方面的能力。