# Deep Reinforcement Learning for Query-Conditioned Video Summarization
## 1 Introduction  
It is estimated that by 2021 it will take a person about 5 million years to watch all the videos that are uploaded each month [1]. In order to process the enormous amount of videos that emerge every day, a tool is required which can significantly reduce the cumbersomeness for digesting the ever-increasing amount of videos. Video summarization helps to address this problem and aims to automatically select a small subset of the frames/shots that captures the most interesting parts in a concise manner [2; 3; 4; 5; 6; 7]. Thus it reduces the time and cost required to analyze video information, and provides significant advantages for efficient video browsing, searching and understanding, and further enhances various down-streaming applications such as video-based question answering [8], robot learning [9] and surveillance data analysis [10; 11]. Different from traditional video summarization which only focuses on video content, query-conditioned video summarization is tasked to generate user-oriented summaries conditioned on a given query in the form of text [4; 12; 13; 14; 15]. As shown in Figure 1, different user queries for the same video will have different summary results, so that the summary can be tailored to different user interests, leading to a better personalized summary. Given a user query that reveals a specific userinterest, query-conditioned video summarization focuses on predicting summaries that are (1) in close correspondence with the semantic meaning of the user query; (2) most representative of the original video, producing a minimal and compact set of video frames/shots. Therefore, this task can be viewed as a step towards personalized video summarization [13]. It also enables more accurate evaluation by addressing the issue that generic video summarization is a highly subjective task where users may have different video-content preferences. Moreover, it enables users to more efficiently search video contents via text with multiple key words instead of only searching for video titles [14]. Queries can be supplied in the form of text and can be either storyline-based [16] or keyword-based. The storyline-based summarization is proposed in order to retrieve video results based on a query that can be represented as a graph. [PERSON] et al. [16] generate a storyline representation consisting of the following four story elements along a time-line: _[Actors, Location, Supporting objects, Events]_. However their method assumes prior knowledge for the list of locations and events in order to train location/event specific classifiers based on web-images, which can be challenging in many situations. Keyword-based summarization requires fewer fine-grained annotations and has been explored in several previous works. In [17; 18], the authors investigated an attention-based personalized summarization technique tailored to Cultural Heritage scenarios by looking for scenes that are similar to web images that match user preferences. However, it is tailored to a cultural heritage scenario, and may not generalize to wider scenarios. More recently, [PERSON] et al. [13] propose a more generalized query-conditioned summarization task including datasets and evaluation metrics. They collect a set of concepts containing representative semantic information for a wide range of commonly used terms such as specific objects, people, and fine-grained entities. The annotation in each video shot is either 0 or 1 indicating the absence/presence of the shot in the summary for a given query. Thus, for each query and each video, the ground truth consists of a binary semantic vector. Due to its general applicability to a wide range of scenarios as well as its personalized query-specific evaluation, our proposed algorithm targets this task. The key challenge for this query-conditioned video summarization task is to identify the relevance between a video and a given query, and simultaneously generate summaries that are of great interest with a minimum number of video shots.