Aiming at the problems of inadequate semantic understanding, improper summary sentences and inaccurate summary in the field of Natural Language Processing (NLP) abstractive automatic summarization, a new automatic summary solution was proposed, including an improved word vector generation technique and an abstractive automatic summarization model. The improved word vector generation technology was based on the word vector generated by the skip-gram method. Combining with the characteristics of abstract, three word features including part of speech, word frequency and inverse text frequency were introduced, which effectively improved the understanding of words. The proposed Bi-MulRnn+ abstractive automatic summarization model was based on sequence-to-sequence (seq2seq) framework and self-encoder structure. By introducing attention mechanism, Gated Recurrent Unit (GRU) gate structure, Bi-directional Recurrent Neural Network (BiRnn) and Multi-layer Recurrent Neural Network (MultiRnn), the model improved the summary accuracy and sentence fluency of abstractive summarization. The experimental results of Large-Scale Chinese Short Text Summarization (LCSTS) dataset show that the proposed scheme can effectively solve the problem of abstractive summarization of short text, and has good performance in Rouge standard evaluation system, improving summary accuracy and sentence fluency.