"Previously on ..." From Recaps to Story Summarization (2405.11487v1)

Abstract: We introduce multimodal story summarization by leveraging TV episode recaps - short video sequences interweaving key story moments from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps and long episodes of 40 minutes. Story summarization labels are unlocked by matching recap shots to corresponding sub-stories in the episode. We propose a hierarchical model TaleSumm that processes entire episodes by creating compact shot and dialog representations, and predicts importance scores for each video shot and dialog utterance by enabling interactions between local story groups. Unlike traditional summarization, our method extracts multiple plot points from long videos. We present a thorough evaluation on story summarization, including promising cross-series generalization. TaleSumm also shows good results on classic video summarization benchmarks.

Multimodal Story Summarization through TV Show Recaps

The paper "“Previously on …” From Recaps to Story Summarization" presents a novel approach to story summarization in multimedia by leveraging TV series recaps. The authors introduce PlotSnap, a dataset encompassing two crime thriller TV series, which harnesses these recaps as critical elements for story summarization. The work expands on traditional summarization tasks by incorporating multimodal inputs, processing entire episodes to generate video-text story summaries rather than single-modality abstractions.

Synopsis of Contributions

The authors make the following notable contributions:

1. PlotSnap Dataset: By focusing on crime thrillers with rich narrative structures and engaging recaps such as TV series "24" and "Prison Break", the dataset bridges a significant gap in existing resources by supporting multimodal story summarization.
2. StoryNarrator Model: This hierarchical model enables the processing of multimodal data by creating compact representations and predicting importance scores across video shots and dialog utterances. The design supports interactions within local story groups and across an entire episode, emphasizing both context and narrative significance.
3. Algorithmic Innovation: The paper introduces a shot-matching algorithm that aligns recap shots with episode sub-stories. This technique extends the utilization of recaps beyond memory aids to becoming an integral part of the storytelling framework.
4. Cross-Modality Evaluation: StoryNarrator demonstrates effective cross-series generalization, extending its applicability beyond initial test series. It performs well on traditional video summarization benchmarks and showcases robustness across varying narratives and genres.

Empirical Evaluation

The model’s performance is evaluated on both traditional benchmark datasets like SumMe and TVSum and the novel PlotSnap dataset. StoryNarrator surpasses state-of-the-art methods in producing multimodal summaries, achieving high Average Precision scores for both video and dialog predictions. This numerical evidence underlines the efficacy of utilizing recaps as comprehensive labels.

Implications and Future Directions

Theoretical Implications: The paper presents a shift in how story summarization can be approached within computational paradigms. By treating video-text summarization as a multimodal interaction space, it challenges existing models that predominantly operate within a single modality.

Practical Implications: For industries such as content streaming and video archiving, the proposed framework can optimize user engagement and enhance retrieval of story content through succinct, context-rich summaries.

Speculation on Future Developments: Future research could expand into exploring recaps in genres beyond thrillers. Additionally, integration with long-form video understanding frameworks may further enhance the summarization task. Moreover, considering the significant strides in LLMs, integrating models like GPT or BERT with video-based frameworks could offer even richer, context-aware summaries.

Conclusion

The approach delineated in this paper underscores the potential of using TV recaps beyond mere narrative aids. The introduction of PlotSnap and the StoryNarrator model sets a precedence for multimodal processing within storytelling contexts, providing a robust framework for future research directions, aligning multimedia processing with advanced AI methodologies.