Paper2Web: Let's Make Your Paper Alive!
Academic project websites can more effectively disseminate research when they
clearly present core content and enable intuitive navigation and interaction.
However, current approaches such as direct Large Language Model (LLM)
generation, templates, or direct HTML conversion struggle to produce
layout-aware, interactive sites, and a comprehensive evaluation suite for this
task has been lacking. In this paper, we introduce Paper2Web, a benchmark
dataset and multi-dimensional evaluation framework for assessing academic
webpage generation. It incorporates rule-based metrics like Connectivity,
Completeness and human-verified LLM-as-a-Judge (covering interactivity,
aesthetics, and informativeness), and PaperQuiz, which measures paper-level
knowledge retention. We further present PWAgent, an autonomous pipeline that
converts scientific papers into interactive and multimedia-rich academic
homepages. The agent iteratively refines both content and layout through MCP
tools that enhance emphasis, balance, and presentation quality. Our experiments
show that PWAgent consistently outperforms end-to-end baselines like
template-based webpages and arXiv/alphaXiv versions by a large margin while
maintaining low cost, achieving the Pareto-front in academic webpage
generation.
68f595bf8589920bf4d320fa
Yuhang Chen
68f595bf8589920bf4d320fb
Tianpeng Lv
68f595bf8589920bf4d320fc
Siyi Zhang
68f595bf8589920bf4d320fd
Yixiang Yin
68f595bf8589920bf4d320fe
Yao Wan
68f595bf8589920bf4d320ff
Philip S. Yu
68f595bf8589920bf4d32100
Dongping Chen
Paper2Web: Let's Make Your Paper Alive!
Academic project websites can more effectively disseminate research when they
clearly present core content and enable intuitive navigation and interaction.
However, current approaches such as direct Large Language Model (LLM)
generation, templates, or direct HTML conversion struggle to produce
layout-aware, interactive sites, and a comprehensive evaluation suite for this
task has been lacking. In this paper, we introduce Paper2Web, a benchmark
dataset and multi-dimensional evaluation framework for assessing academic
webpage generation. It incorporates rule-based metrics like Connectivity,
Completeness and human-verified LLM-as-a-Judge (covering interactivity,
aesthetics, and informativeness), and PaperQuiz, which measures paper-level
knowledge retention. We further present PWAgent, an autonomous pipeline that
converts scientific papers into interactive and multimedia-rich academic
homepages. The agent iteratively refines both content and layout through MCP
tools that enhance emphasis, balance, and presentation quality. Our experiments
show that PWAgent consistently outperforms end-to-end baselines like
template-based webpages and arXiv/alphaXiv versions by a large margin while
maintaining low cost, achieving the Pareto-front in academic webpage
generation.
Paper2Web is a benchmark and evaluation framework for academic webpage generation, featuring PWAgent, an autonomous pipeline that enhances content and layout through MCP tools, outperforming end-to-end baselines.
Large Language Model (LLM) Paper2Web benchmark dataset evaluation framework Connectivity Completeness LLM-as-a-Judge PaperQuiz PWAgent MCP tools academic webpage generation template-based webpages arXiv/alphaXiv versions Pareto-front