Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers (2505.21497v1)

Abstract: Academic poster generation is a crucial yet challenging task in scientific communication, requiring the compression of long-context interleaved documents into a single, visually coherent page. To address this challenge, we introduce the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i)Visual Quality-semantic alignment with human posters, (ii)Textual Coherence-language fluency, (iii)Holistic Assessment-six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv)PaperQuiz-the poster's ability to convey core paper content as measured by VLMs answering generated quizzes. Building on this benchmark, we propose PosterAgent, a top-down, visual-in-the-loop multi-agent pipeline: the (a)Parser distills the paper into a structured asset library; the (b)Planner aligns text-visual pairs into a binary-tree layout that preserves reading order and spatial balance; and the (c)Painter-Commenter loop refines each panel by executing rendering code and using VLM feedback to eliminate overflow and ensure alignment. In our comprehensive evaluation, we find that GPT-4o outputs-though visually appealing at first glance-often exhibit noisy text and poor PaperQuiz scores, and we find that reader engagement is the primary aesthetic bottleneck, as human-designed posters rely largely on visual semantics to convey meaning. Our fully open-source variants (e.g. based on the Qwen-2.5 series) outperform existing 4o-driven multi-agent systems across nearly all metrics, while using 87% fewer tokens. It transforms a 22-page paper into a finalized yet editable .pptx poster - all for just $0.005. These findings chart clear directions for the next generation of fully automated poster-generation models. The code and datasets are available at https://github.com/Paper2Poster/Paper2Poster.

• The paper introduces an automated system for converting scientific papers into conference posters by integrating multimodal AI techniques such as NLP, computer vision, and generative layout design.
• It outlines a comprehensive pipeline from content extraction and summarization to layout generation, emphasizing modular design and optimization methods.
• The research highlights practical challenges in poster design automation including diverse visual content handling and preserving the nuanced narrative of the original paper.

The PDF for the paper "Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers" (2505.21497) is currently unavailable, which prevents a direct summary of its specific methods and findings. However, based on its title, the research likely focuses on creating an automated system to convert scientific papers into conference-style posters. This is a complex task involving natural language processing, computer vision, and layout design.

Here's an outline of how such a "Paper2Poster" system might be conceptualized and implemented, along with practical considerations:

Goal of "Paper2Poster" Systems

The primary goal is to automate the generation of visually appealing and informative posters from the content of scientific papers. This involves:

1. Extracting salient information (text, figures, tables) from the source paper.
2. Summarizing and reformatting this information for a concise poster presentation.
3. Arranging the selected content into a well-structured and aesthetically pleasing layout.
4. Handling the multimodal nature of both the input (text, images in papers) and output (text, images, layout in posters).

Proposed System Architecture

A Paper2Poster system could be broken down into several key modules:

graph TD A[Scientific Paper (PDF/LaTeX/XML)] --> B{1. Input Parsing & Preprocessing}; B --> C{2. Content Extraction & Structuring}; C -- Text --> D{3a. Text Analysis & Summarization}; C -- Figures/Tables --> E{3b. Visual Element Analysis}; D --> F{4. Content Selection & Adaptation}; E --> F; F --> G{5. Layout Generation & Design}; G -- Poster Sections & Elements --> H{6. Multimodal Assembly}; H --> I[Generated Poster (PDF/PPTX)]; I --> J{Optional: User Feedback & Iteration};

1. Input Parsing & Preprocessing:

• Task: Convert the input paper (commonly PDF, but could be LaTeX or XML/JATS) into a structured format.
• Implementation:
  • For PDFs: Tools like Grobid can parse PDFs into structured XML (TEI format), identifying sections, paragraphs, figures, and tables.
  • For LaTeX: Custom parsers or tools that convert LaTeX to a structured representation.
• Considerations: Robustly handling diverse PDF layouts and LaTeX styles is challenging.

2. Content Extraction & Structuring:

• Task: Isolate textual content (title, abstract, sections, captions) and visual elements (figures, tables).
• Implementation:
  • Text: Standard text processing libraries (e.g., Python's re for regex, BeautifulSoup for XML/HTML parsing if applicable).
  • Figures/Tables: PDF parsing tools often provide bounding boxes. Image extraction libraries (e.g., PyMuPDF) can then be used. Optical Character Recognition (OCR) might be needed for text within images if not extractable.

3. Content Analysis:

• 3a. Text Analysis & Summarization:
  • Task: Identify key information, generate summaries for sections (Introduction, Methods, Results, Conclusion), and extract key phrases.
  • Implementation:
    • Summarization:
      • Extractive: TF-IDF, TextRank, LexRank, or BERT-based sentence scoring.
      • Abstractive: Fine-tuned sequence-to-sequence models like BART, T5, or Pegasus on scientific paper datasets (e.g., arXiv, PubMed).

        # Pseudocode for extractive summarization def extractive_summary(text, num_sentences): sentences = sentence_tokenize(text) sentence_vectors = encode_sentences_with_bert(sentences) similarity_matrix = calculate_cosine_similarity_matrix(sentence_vectors) ranked_sentences = page_rank_on_similarity_matrix(similarity_matrix) top_sentences = get_top_n_sentences(ranked_sentences, num_sentences) return " ".join(top_sentences)

    • Keyphrase Extraction: Models like KeyBERT, YAKE!, or graph-based algorithms.
• 3b. Visual Element Analysis:
  • Task: Select the most relevant figures and tables. Potentially simplify complex visuals or analyze their content.
  • Implementation:
    • Heuristics: Prioritize figures/tables mentioned frequently in the results/discussion, or those with informative captions.
    • ML Models: Train a classifier to predict figure importance based on features like caption length, image complexity, or context in the paper.
    • Figure-Caption Matching: Use multimodal models (e.g., CLIP embeddings) to ensure captions accurately describe figures.

4. Content Selection & Adaptation:

• Task: Decide which summarized text blocks and visual elements should appear on the poster, considering space constraints. Adapt content for conciseness.
• Implementation:
  • Rule-based systems: Define rules for including essential sections (e.g., a short intro, key results, main conclusions, 1-2 key figures).
  • Optimization: Formulate as an optimization problem to maximize information coverage under layout constraints.

5. Layout Generation & Design:

• Task: Arrange the selected content elements (text boxes, images) onto a poster canvas. This involves determining positions, sizes, and flow.
• Implementation Approaches:
  • Template-based: Use predefined poster templates and fill them with content. Limited flexibility.
  • Rule-based/Constraint-based: Define design rules (e.g., alignment, spacing, column usage) and use a solver to find a valid layout.

    # Pseudocode for a simple rule-based placer def place_elements(elements, poster_template): placed_elements = [] current_y_position = poster_template.margin_top for section_name, content_blocks in elements.items(): # Place section title # Place content blocks (text, images) within columns # Update current_y_position pass return placed_elements

  • Generative Models: Train models (e.g., GANs, VAEs, diffusion models) on datasets of existing posters. These models could learn to generate layouts conditioned on the input content. For example, LayoutGAN or its variants could be adapted.
    • Input to model: Set of elements with desired rough sizes, types (text/image), and semantic importance.
    • Output: Coordinates and dimensions for each element.
  • Optimization Algorithms: Use algorithms like genetic algorithms or simulated annealing to optimize a layout based on an objective function scoring aesthetics, readability, and information hierarchy.

6. Multimodal Assembly & Styling:

• Task: Combine the content and layout information to render the final poster. Apply consistent styling (fonts, colors, branding).
• Implementation:
  • Use libraries for programmatic document generation (e.g., ReportLab for PDF in Python, python-pptx for PowerPoint).
  • CSS-like styling rules can be applied if the intermediate representation supports it.

Implementation Considerations

• Datasets:
  • Scientific Papers: Large corpora like arXiv, PubMed Central, ACL Anthology are needed for training text processing models.
  • Posters: A significant dataset of well-designed scientific posters, ideally with annotations linking poster elements back to source papers, would be invaluable for training layout models. This is often a major bottleneck.
• Computational Requirements:
  • LLMs for summarization and multimodal models require substantial GPU resources for training and fine-tuning.
  • Inference for generative layout models can also be demanding.
• Evaluation:
  • Content: ROUGE, BERTScore for summaries; precision/recall for key information.
  • Layout: Difficult to automate. Metrics could include alignment scores, white space usage, element overlap. Human evaluation by designers or researchers is crucial.
  • Overall Poster Quality: Subjective assessment, often via user studies (e.g., rating clarity, aesthetics, informativeness).
• Modularity: A modular design allows for easier experimentation and improvement of individual components (e.g., swapping out different summarization models or layout engines).
• User Interaction: For practical use, an interactive system where users can refine the automatically generated poster (e.g., edit text, reposition elements, swap figures) would be highly beneficial.

Potential Limitations and Challenges

• Subjectivity of Design: What constitutes a "good" poster is subjective and can vary by field and personal preference.
• Content Nuance: AI may struggle to capture subtle nuances or the core narrative thread that a human author instinctively highlights.
• Handling Diverse Visuals: Scientific figures can be highly complex and diverse (graphs, diagrams, photos, equations). Standardizing their processing is hard.
• Implicit Knowledge: Authors use implicit knowledge about their field and audience when designing posters, which is difficult for AI to replicate.
• Over-Simplification or Misrepresentation: Automated summarization or content selection might inadvertently distort the original research findings.

Potential Applications

Beyond conference posters, the technologies developed for a "Paper2Poster" system could be applied to:

• Generating graphical abstracts.
• Automating slide generation for presentations based on papers.
• Creating summaries for different audiences.
• Assisting in science communication by making research more accessible.

In summary, while the specific approach of "Paper2Poster" (2505.21497) is unknown due to the unavailable PDF, the problem it addresses is significant. Building such a system requires a sophisticated pipeline integrating advanced AI techniques from NLP, vision, and generative modeling, along with careful consideration of design principles and user needs.