To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of actors. Existing works benchmark this challenge as a normal video captioning task via some simplifications, such as removing role names and evaluating narrations with ngram-based metrics, which makes it difficult for automatic systems to meet the needs of real application scenarios. To narrow this gap, we construct a large-scale Chinese movie benchmark, named Movie101. Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking. External knowledge, such as role information and movie genres, is also provided for better movie understanding. Besides, we propose a new metric called Movie Narration Score (MNScore) for movie narrating evaluation, which achieves the best correlation with human evaluation. Our benchmark also supports the Temporal Narration Grounding (TNG) task to investigate clip localization given text descriptions. For both two tasks, our proposed methods well leverage external knowledge and outperform carefully designed baselines. The dataset and codes are released at https://github.com/yuezih/Movie101.

该研究提出了一个大型的中文电影评测基准Movie101，旨在为视障人士提供自动电影叙述系统。在该基准中，MCN任务要求模型为没有演员说话的完整电影片段生成角色感知的叙述段落。在使用电影流派和角色信息等外部信息的同时，该研究提出了用于电影叙述评估的新指标MNScore，从而使模型评估更接近人工评估。该研究还支持TNG任务，以研究相应电影片段与文本描述之间的精确对齐问题。

Movie101: 一个新的电影理解基准