## 2 Related Work
### End-to-End Model
A technique to generate numerous sentences from the visual data has been proposed by [PERSON] et al. [14]. The strategy expanded the RNN model by past and exact next dissemination with a non-linear layer. This technique can deal with vulnerability by applying a hidden state. The model additionally neglected to catch the high-level temporal structure of the visual frames. Another approach can be found, which is a double-stream RNN for a video description in a textual formation [19]. The method focused on the hidden state of both visual and semantic streams to produce meaningful sentences from the visual contents. The structure incorporated a semantic component extraction strategy and twisted it with word embedding. At that point, the visual and semantic stream together produced an important textual description from a visual scene. Another approach that included adaptive attention and mixed loss optimization for performing video captioning was presented by [PERSON] et al. [21]. The technique included a reinforced adaptive attention mechanism. The procedure was likewise prepared in the word-level loss and sentence-level loss model. The strategy additionally tackled the biased issue of those sorts of losses. A stateful human-centered visual captioning system that can extract facial and visual features from the visual contents and pass through a two-layer LSTM with an attention mechanism to produce efficient video caption in which the model was able to extract human actions and objects [22]. An interesting method was proposed using a refocused video encoder [23]. The method with refocused RNN with spatial-visual features was introduced for better video captioning. The refocused video encoder looked up two times with predicted key features to avoid unwanted temporal information. The spatial information from the different regions was passed through a decoder with detailed features. A semantic enhanced encoder-decoder network method with LSTM was used by researchers in [24]. The method presented extracts motion features, appearance features, and global features for video description generations. The method also introduced reinforcement learning, which can ensure a better quality of sentence generation in previous research. The structure presented in [25], incorporates an action module and motion module for sports video captioning, which is ready to perceive human-related activities with objects during sports. The structure also introduced a pose attribute detection module and a description generation module, the entire system, followed by an attention mechanism. The content branch and the semantic branch encoder were introduced by [PERSON] et al. [26]. The method illustrated a very promising result in terms of two different datasets. The RNN based decoder with soft attention produces the semantic sentence for visual inputs. A model that can produce better captioning due to maintaining the sentences' semantic attention is presented by [PERSON] et al. [27]. The model introduced two independent attention mechanisms to point the soft attention through visual features and attributes another attention focused on visual signals and text documentation. The framework proposed in [28] introduced an extended version of the encoder-decoder system to overcome the manual selection process. A modal attention network can dynamically point out the important features from the video frames and generate a sentence. [PERSON] et al. [29] proposed a video to text summary method, which also generated the textual caption of videos, and then they again summarize the generated sentences to produce a short description of visual content. Those methods are the footprint of various visual summarization methods. A joint model to generate video descriptions and produce abstractive summarization from those visual portrayals that can deliver visual captioning and summarization is depicted in [31]. It is one of the cutting edge and productive habits of textual summarization from visual content.