  Recall the classical text generation works, the generation framework can be
briefly divided into two phases: \textbf{idea reasoning} and \textbf{surface
realization}. The target of idea reasoning is to figure out the main idea which
will be presented in the following talking/writing periods. Surface realization
aims to arrange the most appropriate sentence to depict and convey the
information distilled from the main idea. However, the current popular
token-by-token text generation methods ignore this crucial process and suffer
from many serious issues, such as idea/topic drift. To tackle the problems and
realize this two-phase paradigm, we propose a new framework named Sentence
Semantic Regression (\textbf{SSR}) based on sentence-level language modeling.
For idea reasoning, two architectures \textbf{SSR-AR} and \textbf{SSR-NonAR}
are designed to conduct sentence semantic regression autoregressively (like
GPT2/3) and bidirectionally (like BERT). In the phase of surface realization, a
mixed-granularity sentence decoder is designed to generate text with better
consistency by jointly incorporating the predicted sentence-level main idea as
well as the preceding contextual token-level information. We conduct
experiments on four tasks of story ending prediction, story ending generation,
dialogue generation, and sentence infilling. The results show that SSR can
obtain better performance in terms of automatic metrics and human evaluation.
