DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents (2101.11796v4)

Abstract: Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner. Can machines learn to emulate this laborious process? We present a novel task and approach for document-to-slide generation. Solving this involves document summarization, image and text retrieval, slide structure and layout prediction to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides. To help accelerate research in this domain, we release a dataset about 6K paired documents and slide decks used in our experiments. We show that our approach outperforms strong baselines and produces slides with rich content and aligned imagery.

• The paper introduces a hierarchical sequence-to-sequence approach that automatically converts scientific documents into presentation slides.
• The paper releases a substantial dataset of nearly 6,000 document-slide pairs to enable robust benchmarking and further research.
• The paper employs tailored evaluation metrics like Slide-Level ROUGE and mIoU to demonstrate its method's superiority over existing baselines.

Automatic Presentation Slide Generation from Scientific Documents: An Analysis

The paper "DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents" by Tsu-Jui Fu and colleagues introduces a novel task in the transformative intersection of NLP and computer vision (CV). This paper discusses an approach designed to automatically generate presentation slides from scientific documents, addressing complex multimodal reasoning tasks comprehensively. It proposes a hierarchical sequence-to-sequence model, which is adept at handling this complex task in an end-to-end manner. Furthermore, the authors release a novel dataset comprising approximately 6,000 paired documents and slide decks, highlighting their commitment to fostering further research in this emerging domain.

Core Contributions

1. Hierarchical Sequence-to-Sequence Approach: The hierarchical structure of this model forms the crux of their approach. It accounts for the document's intrinsic structuring and the multimodal nature of the data (i.e., text and figures). The authors design the architecture to 'read' a scientific paper and 'summarize' it into slides, incorporating elements such as paraphrasing and layout prediction to bond the multimodal inputs effectively.
2. Significant Dataset Contribution: With 5,873 pairs of documents and slide decks, theirs is a substantial contribution that the authors have made publicly available. This dataset, spanning a range of domains in computer vision, natural language processing, and machine learning, allows for extensive benchmarking and evaluation in automatic slide generation.
3. Proposed Evaluation Metrics: Understanding the lack of existing benchmarks and evaluation metrics in this field, the paper introduces metrics tailored to evaluate the quality of the generated slides. These include Slide-Level ROUGE (ROUGE-SL), Longest Common Figure Subsequence (LC-FS), Text-Figure Relevance (TFR), and mean Intersection over Union (mIoU) for quantifying the layout quality.

Experimental Results

The authors claim that their model surpasses existing baselines, demonstrating the effectiveness of their hierarchical approach. By leveraging a combined sequence-to-sequence and multimodal methodology, they achieve strong numerical performance on the proposed novel metrics. Their method, especially after including post-processing components and paraphrasing, represents a comprehensive framework that improves both the textual synopsis and visual-content alignment in the accumulated slide decks.

Practical Implications and Future Directions

The demonstrated ability to produce semi-autonomous slide decks holds significant potential for applications across academia and industry, greatly improving the productivity of professionals who frequently create presentations from dense technical documents. Moving forward, this could reduce the cognitive load and time overhead associated with preparing educational or industry presentations, making the document-to-slide conversion more accessible and less time-consuming.

Future developments can focus on refining the model's robustness across different scientific domains and enhancing the quality of predictions concerning unseen topics. Continued exploration into incorporating sophisticated graphic design considerations into layout predictions could further enhance the usability of this automated solution.

Concluding Remarks

This paper expands the dialogue within the AI discipline concerning integrating diverse modalities into coherent, human-centric tools. Though impressive in its achievements, ongoing advancements in NLP and CV would enable refining this approach further, potentially encapsulating even broader aspects of human-AI collaboration in creative and professional settings.