An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions.

In recent years among data hiding technologies, Reversible Data Hiding(RDH) technology has attracted widespread interest and application, which is to hide the secret information in a carrier image and recover the original carrier image losslessly to extract the secret information. Current research on RDH algorithms mainly involving frequency domain, spatial domain, and encryption domain. Based on the Prediction-Error Expansion(PEE) methods, as spatial domain approaches, achieved great progress in the past decade. However, there is a defect in the state-of-the-art methods that with the embedded payload increased, the distortion rate of the cover image increased simultaneously. To solve the problem, we proposed a refined reversible data hiding algorithm based on the PEE method with simple implementation. We improved an effective predictor that all the remaining pixels can be predicted in the embedding process, except for those in the first row, the first column, the last row, and the last column in the original image. The extraction process is the reverse of the embedding process that the embedded information and the original carrier is restored without damage. Our work utilized the correlation between image pixels better to solve the inherent contradiction between payload and distortion rate in the state-of-the-art data hiding algorithms. Proven through experiments, our method achieved a large embedding capacity while keeping the image distortion rate and computing complexity low.

Reversible data hiding (RDH) is a technique that slightly alters digital media (e.g. images or videos) to embed secret messages while the original digital media can be completely recovered without any error after the hidden messages have been extracted. In the past more than one decade, hundreds of RDH algorithms have been reported, and among these algorithms, the difference histogram shifting (DHS) based methods have attracted much attention. With DHS-based RDH, high capacity and low distortion can be achieved efficiently. But there occurs one problem that, with DHS, the difference values to embed secret bits are explored, and the other difference values are shifted to create vacant spaces, it will cause the difference value histogram changing significantly and draw the attention of steganalyzers. So, this paper proposed a new idea for RDH based on the difference value and with statistical features maintained (SFM) with simple implementation and high scalability, we embed the secret messages by keeping the difference values that need to be modified in the original range, and the other difference values would not be shifted. In addition, we need the original difference values as the key to extract the secret messages. In order to expand the embedding capacity further, we designed two algorithms that embed message in two different difference values and four different difference values, and these two methods are named SFM_A and SFM_B respectively. SFM_B can support greater amount of embedded message than SFM_A, but brings greater changes to the original image, which could lead to the decline of PSNR and SSIM. The experimental results show that through our method, the histogram of difference values is well maintained, and the degree of distortion of the image is improved at the same time.