  In the contemporary information era, significantly accelerated by the advent
of Large-scale Language Models, the proliferation of scientific literature is
reaching unprecedented levels. Researchers urgently require efficient tools for
reading and summarizing academic papers, uncovering significant scientific
literature, and employing diverse interpretative methodologies. To address this
burgeoning demand, the role of automated scientific literature interpretation
systems has become paramount. However, prevailing models, both commercial and
open-source, confront notable challenges: they often overlook multimodal data,
grapple with summarizing over-length texts, and lack diverse user interfaces.
In response, we introduce an open-source multi-modal automated academic paper
interpretation system (MMAPIS) with three-step process stages, incorporating
LLMs to augment its functionality. Our system first employs the hybrid modality
preprocessing and alignment module to extract plain text, and tables or figures
from documents separately. It then aligns this information based on the section
names they belong to, ensuring that data with identical section names are
categorized under the same section. Following this, we introduce a hierarchical
discourse-aware summarization method. It utilizes the extracted section names
to divide the article into shorter text segments, facilitating specific
summarizations both within and between sections via LLMs with specific prompts.
Finally, we have designed four types of diversified user interfaces, including
paper recommendation, multimodal Q\&A, audio broadcasting, and interpretation
blog, which can be widely applied across various scenarios. Our qualitative and
quantitative evaluations underscore the system's superiority, especially in
scientific summarization, where it outperforms solutions relying solely on
GPT-4.
