Efficient Movie Script Summarization by Identifying Salient Scenes

Temel Kavramlar

Automatically identifying salient scenes in movie scripts and using them to generate more accurate and informative movie summaries.

Özet

The paper introduces a dataset called MENSA that contains human-annotated salient scenes for 100 movie scripts and their corresponding Wikipedia plot summaries. It then proposes a two-stage approach for movie script summarization:

Scene Saliency Classification:

The authors train a transformer-based model to classify which scenes in a movie script are salient, i.e., important for understanding the overall narrative.
They evaluate different automatic alignment methods to generate silver-standard labels for scene saliency and show that their model outperforms baselines on the gold-standard MENSA dataset.

Summarization using Salient Scenes:

The authors use the output of the scene saliency model to generate movie summaries, feeding only the salient scenes to a Longformer Encoder-Decoder (LED) model.
Experiments show that this approach outperforms state-of-the-art baselines on the Scriptbase corpus, achieving new SOTA results on ROUGE and BERTScore metrics.
The authors also evaluate the summaries using a question-answering based metric (QAEval) and find that their model generates summaries that are more factually consistent with the original movie scripts.
The key insight is that by focusing the summarization model on only the salient scenes, it can generate more accurate and informative movie summaries, while also reducing the computational and memory requirements compared to using the full movie script.

İstatistikler

Movie scripts typically comprise a large number of scenes, but only a fraction of these scenes are salient for understanding the overall narrative.
The average length of a movie script is 110 pages, making them challenging to summarize using large language models.

Alıntılar

"Abstractive summarization is the process of reducing an information source to its most important content by generating a coherent summary."
"Movie scripts are structured in terms of scenes, where each scene describes a distinct plot element and happening at a fixed place and time, and involving a fixed set of characters."

Önemli Bilgiler Şuradan Elde Edildi

Select and Summarize

by Rohit Saxena... : arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03561.pdf

Daha Derin Sorular

How can the definition of scene saliency be extended beyond just mentions in the summary, to capture other aspects of narrative importance?

The definition of scene saliency can be extended by considering various factors beyond just mentions in the summary. One approach could be to incorporate the presence of key characters or pivotal events in a scene as indicators of saliency. Scenes that contribute significantly to character development, plot progression, or thematic elements could be deemed salient. Additionally, visual elements, such as unique settings or impactful visual storytelling, could also be considered in determining scene saliency. By integrating these aspects into the definition, a more comprehensive understanding of narrative importance can be achieved.

How can other techniques beyond content selection be used to improve the factual consistency of generated movie summaries?

Beyond content selection, techniques such as fact-checking modules and knowledge verification mechanisms can be employed to enhance the factual consistency of generated movie summaries. Fact-checking modules can verify the accuracy of information presented in the summary by cross-referencing it with reliable sources or databases. Knowledge verification mechanisms can ensure that the summary aligns with established facts or canonical information related to the movie. Additionally, incorporating context-awareness and logical reasoning capabilities into the summarization model can help maintain factual consistency by ensuring coherence and accuracy in the generated summaries.

How could this approach be adapted to summarize other types of long-form narrative texts, such as novels or TV show scripts?

This approach can be adapted to summarize other types of long-form narrative texts, such as novels or TV show scripts, by modifying the input data and training the models accordingly. For novels, the scene saliency dataset can be created by aligning key plot points or chapters with summaries. The scene saliency classification model can then be trained on this dataset to identify salient segments in the novel. Similarly, for TV show scripts, the alignment between scenes and summaries can be established to determine saliency. By adjusting the input data and training process to suit the specific characteristics of novels or TV show scripts, the approach can effectively summarize these types of long-form narrative texts.