Abstract: Automatic document summary is an important task in the field of natural language processing.Limited by the difficulty of accurately understanding the semantics of documents,most of the documents are sorted by artificial features,such as word frequency and keywords,to extract the abstract.Inspired by the theory of sparse representation,a dynamic semantic space partition algorithm based on sparse representation is proposed.The algorithm performs dictionary learning on the initially divided semantic subspace,uses the obtained dictionary to sparsely reconstruct the sentence vector.Dynamically adjusts it to the division which has the smallest reconstruction error.Iteratively realizes the re-division of the semantic space.For abstracting sentences in the divided semantic subspace,an automatic extraction algorithm based on sparse similarity ranking is proposed.All sentence vectors in each semantic subspace are viewed as dictionary atoms.Through sparse reconstruction,the sparse similarity can be obtained which reflects the degree of semantic representation of one sentences to others.The cumulative sparse similarity of each sentence to other sentences is used as a metric to measure the ability of the sentence to represent the spatial semantic information.Ranking the cumulative sparse similarity,and then extract the required top N sentences.The experimental results on the travel review data set of popular attractions on the TripAdvisor website show that the semantic space reconstruction error can be rapidly reduced after5 iterations,remain stable which shows the convergence.Except for effectively reduce the reconstruction error by nearly 17%,the algorithm is also not sensitive to data dimensions.The proposed summary avoids repeated abstraction of redundant and highly repetitive text,which is an effective multi-document automatic summarization method.

Key words: Automatic summarization, Dictionary learning, Sparse reconstruction