Extensive researches have demonstrated the effectiveness of Image-to-Sequence (img2seq) approaches in table structure recognition (TSR) task. However, when dealing with long tables, these methods always suffer from the inherent limitations of their attention mechanism, which hinders the further progress of this technical route. Based on the repetitive nature of table body structures, we present a novel approach for compressing table HTML code, resulting in a significant reduction in the overall length of the HTML code by more than 65%. Additionally, to address the relatively complex structure of table heads and the dense arrangement of rows and cells in long tables, we integrate coverage information into the transformer decoder. Experiments show that our method achieves performance comparable to the state-of-the-art methods with a simple model structure. What’s more, our method effectively mitigates the performance degradation commonly observed in img2seq methods when the length of the target sequence increases.