Text Generation with Fusion Sentiment and Semantic Features Under Sliding Window
YUAN Weidong
SHENG Kuang
CHEN Pinghua
Abstract：Traditional text generation has shortcomings such as unstable sentiment polarity of generated short texts,one-sided semantics,and single comments.This method first uses TextRank to extract important sentences,then uses sliding windows to se-lect key sentences to form key sentence groups,then uses RoBERTa and Sentence-BERT to obtain emotional and semantic fea-tures,and finally uses GPT-2 to fuse these features to decode and generate high-quality short text.Experiments show that on the Nanfang Daily's news comment dataset,NLPCC-2017 dataset and CNewSum dataset,this paper has improved the three indicators of ROUGE.At the same time,the effectiveness of each component is verified by ablation experiments.
Keywords：text GenerationGPT-2TextRanksliding windowRoBERTaSentence-BERT
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：7( 3392-3398 )