Abstract:The controllable text summary models can generate summaries that conform to user preferences. Previous summary models focus on controlling a certain attribute alone, rather than the combination of multiple attributes. When multiple control attributes are satisfied, the traditional Seq2Seq multi-attribute controllable text summary model cannot integrate all control attributes, accurately reproduce key information in the texts, and handle words outside the word lists. Therefore, this study proposes a model based on the extended Transformer and pointer generator network (PGN). The extended Transformer in the model extends the Transformer single encoder-single decoder model form into a dual encoder with dual text semantic information extraction and a single decoder form that can fuse guidance signal features. Then the PGN model is employed to select the source from the source copy words in the text or adopt vocabulary to generate new summary information to solve the OOV (out of vocabulary) problem that often occurs in summary tasks. Additionally, to efficiently complete position information encoding, the model utilizes relative position representation in the attention layer to introduce sequence information of the texts. The model can be leveraged to control many important summary attributes, including lengths, topics, and specificity. Experiments on the public dataset MACSum show that compared with previous methods, the proposed model performs better at ensuring the summary quality. At the same time, it is more in line with the attribute requirements given by users.