  Geometry problem solving (GPS) is a high-level mathematical reasoning
requiring the capacities of multi-modal fusion and geometric knowledge
application. Recently, neural solvers have shown great potential in GPS but
still be short in diagram presentation and modal fusion. In this work, we
convert diagrams into basic textual clauses to describe diagram features
effectively, and propose a new neural solver called PGPSNet to fuse multi-modal
information efficiently. Combining structural and semantic pre-training, data
augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge
of geometry theorems and geometric representation, and therefore promotes
geometric understanding and reasoning. In addition, to facilitate the research
of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K,
labeled with both fine-grained diagram annotation and interpretable solution
program. Experiments on PGPS9K and an existing dataset Geometry3K validate the
superiority of our method over the state-of-the-art neural solvers. Our code,
dataset and appendix material are available at
\url{https://github.com/mingliangzhang2018/PGPS}.
