It can be seen from the above, the seq2seq model converts the input sequence into a fixed-length semantic vector C, and then decodes the semantic vector C to generate a sequence Y [11]. But the length of the semantic vector C is fixed, so **data** loss will occur when the sequence is generated. This article adds an attention mechanism to the seq2seq model. In the encoding stage, the input sequence can not only be transformed into a fixed-length semantic vector C, the input sequence X i will correspond to a different semantic vector C i . In this way, the **data** loss will be reduced when generating the sequence [10] (Figure 4). The abstract generated in this way is more similar to the abstract generated after manual re-understanding, which is more in line with the author's original intention and high intelligibility [12]. At the same time, it conforms to the characteristics of English long reading.