Source-linked AI summary
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Aidean Sharghi, Jacob S. Laurel, Boqing Gong
TL;DR
Video summarization must handle differing user preferences while remaining evaluable at scale. The paper addresses these issues with query-focused memory-network summarization, dense concept annotations, and a semantic evaluation metric. Its experiments report that the proposed summarizer outperforms comparable methods, including by more than 10% average F1 in one comparison.
Problem
User subjectivity makes it difficult for one summarizer to fit all users and makes summarizer evaluation challenging, especially when existing metrics rely on visual or temporal representations.
Method
The paper combines a memory-network parameterized sequential determinantal point process with a new dataset of dense per-video-shot concept annotations and a concept-based evaluation metric.
Results
More than 10% average F1: the proposed summarizer outperforms the other two methods in the reported query-focused comparison.
Takeaways & Limitations
The paper supports evaluating video summaries through semantic information that humans can perceive and personalizing summaries with text queries.
Takeaways & Limitations
Existing automated evaluation metrics may require exact key-unit matches or rely on visual and temporal representations that do not explicitly encode human-perceived information.
Abstract
from arXiv · showhide
Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.
1. Introduction
The paper addresses user subjectivity in video summarization by exploring query-focused summaries and by proposing semantic concept annotations for evaluation. It combines a query-conditioned memory-network summarizer with a concept-based evaluation method.
- Motivation: User subjectivity creates personalization and evaluation challenges because users prefer different summaries.The paper states that a single summarizer may not fit all users and that evaluating summarizer performance is difficult.
- Motivation: Query-focused video summarization introduces user preferences as text queries about the video.The paper investigates whether different queries produce distinct summaries and how much those summaries differ.
- Dataset and evaluation: The paper builds a new query-focused dataset with dense per-video-shot concept annotations.Each shot’s semantic information is represented using concept annotations rather than relying only on conventional summary labels.
- Dataset and evaluation: The proposed evaluation metric compares summaries using semantic information that humans can perceive rather than visual features or temporal overlap.The metric is based on binary semantic vectors representing concepts present in video shots.
- Model: The summarizer uses a memory network parameterized sequential determinantal point process to attend user queries to video frames and shots.The approach is designed to address query-focused summarization without the costly user supervision or pre-trained concept detectors described in the paper.
- Experiments: The paper reports extensive experiments comparing its summarizer with existing methods and analyzes the dataset and evaluation method.The paper organizes its dataset, metric, model, experimental setup, and quantitative results as separate components.
2. Related Work
The related work positions the paper at the intersection of query-focused summarization, semantic shot annotation, memory networks, and determinantal point processes. Its approach extends prior personalization work with a densely tagged dataset and a memory-network-based model.
- Query-focused summarization: This work extends prior efforts on personalizing video summarizers through a new dataset with dense per-video-shot concept tagging.The authors state that the memory-network summarizer also requires less training supervision than the earlier hierarchical model.
- Semantic annotations: Concept annotations provide more comprehensive semantic coverage than single-sentence captions for video shots.The paper contrasts its user-annotated semantic vectors with the captions used by Yeung et al.
- Memory networks: The memory network treats temporal video sequences as facts and attends query information to them.A sequential determinantal point process is placed on top of the memory network to promote diversity in summaries.
- Determinantal point processes: The model extends determinantal point processes by combining their diversity modeling with a memory neural network.DPPs define distributions over subsets that encourage diversity among selected items.
3. Dataset
The dataset is designed for query-focused video summarization, combining dense shot-level concept annotations with user summaries for diverse multi-concept queries. It uses these annotations to support semantic evaluation and documents how queries change summary content and length.
- Dataset design: The dataset builds on 3–5-hour consumer videos from the UT Egocentric dataset, which contain diverse everyday events.The authors choose this source because its uncontrolled, consumer-grade videos make summarization both desirable and challenging.
- Concept dictionary and queries: Its concept dictionary is designed to cover multiple semantic levels and contains 48 concepts tailored to daily-life videos.The dictionary combines caption nouns with SentiBank concepts, prunes weak or redundant terms, and adds frequently searched terms.
- Concept dictionary and queries: For each video, the dataset defines 46 two- or three-concept queries spanning co-occurring, separately occurring, partially occurring, and absent concepts.The queries are intended to represent varied search situations rather than singleton preferences.
- Shot tagging: Annotators label each 5-second shot with present concepts, and the union of three workers’ labels forms its binary semantic vector.Workers inspect five uniformly extracted frames per shot; unioning annotations captures subtle concepts missed by individual annotators.
- Semantic evaluation: The evaluation matches shots using intersection-over-union of their concept sets and computes precision, recall, and F1 through maximum-weight bipartite matching.This replaces low-level visual edge weights with similarities defined over user-annotated semantic vectors.
- User summaries: Three trained student volunteers produce summaries for every query-video pair, while oracle summaries aggregate their common shots and greedy marginal F1 gains.The collection also provides budgeted 20-shot and 10-shot summaries for future research.
- User summaries: Queries strongly alter summaries: the same user preserves main events but selects query-specific shots, producing substantial variation in summary lengths.Figure 4 contrasts the queries {HAT, PHONE} and {FOOD, DRINK}; Table 2 reports average lengths and standard deviations across queries.
4. Approach
The approach conditions a sequential determinantal point process on a query and uses a memory network to model query-related and contextual shot importance. It attends the query over sampled frames, forms query-conditioned shot representations, and learns the summarizer from user summaries.
- Query Conditioned Sequential DPP: The sequential DPP defines the joint probability of selected shot subsets through conditional distributions across video segments.The conditional distribution for each segment depends on the current segment, the previous selection, and the query.
- Query Conditioned Sequential DPP: The model partitions a video into segments and applies a query-conditioned sequential DPP to select shot subsets sequentially.Each segment contains 10 five-second shots, and the DPP variable for segment t selects a subset from that segment.
- Memory Network: The memory network jointly models query-related importance through attention and contextual importance through similarities among shots in the kernel.This design accounts for shots that are either relevant to the query or contextually important for representing a prominent event.
- Memory Network: The memory network samples eight frames per shot as facts and attends the query over them to estimate shot relevance.Frames and the binary query are embedded, then a dot product followed by softmax produces attention weights for the frames.
- Memory Network: The attended frame embeddings are assembled into a query-conditioned shot representation used to parameterize the DPP kernel.The resulting representation encodes the shot’s relevance strength to the query and contributes to the kernel’s shot-importance modeling.
- Learning and Inference: The full summarizer is trained by maximizing the log-likelihood of user summaries and then selects shots sequentially using the learned model.Optimization uses stochastic gradient descent with mini-batches over the embedding matrices, with learning settings chosen on a validation set.
5. Experimental Results
The experiments evaluate query-focused and generic video summarization, component contributions, and a concept-based evaluation metric. The proposed approach outperforms comparison methods and its metric responds predictably to corrupted summaries.
- Query-focused video summarization: The experiments compare query-focused summarizers using precision, recall, and F1 across held-out videos.Each video is used in turn for testing, with average results reported.
- Query-focused video summarization: More than 10% average F1 separates the proposed approach from the other two query-focused summarizers.Video 4 is challenging for all methods, while Video 2 produces slightly longer summaries with the proposed model.
- Component-wise analyses: The memory-network attention mechanism improves performance on all four videos relative to uniform attention.Removing the final embedding matrix or changing the embedding size also lowers performance, indicating complementary components.
- Generic video summarization: The proposed method achieves the best overall generic-summarization performance even when baselines receive oracle summary lengths.The comparison includes SubMod and Quasi for queries with no relevant video shots.
- Evaluation metric: Dense concept annotations replace unstable caption-based evaluation with an intersection-over-union metric focused on semantic information.The annotations provide broader shot coverage than captions and support efficient pairwise evaluation.
- Evaluation metric: Metric recall decreases almost linearly when shots are randomly removed from user summaries, unlike ROUGE-SU4 recall.This behavior is presented as evidence that the proposed metric responds smoothly to summary corruption.
6. Conclusion
The paper studies subjectivity in video summarization through a densely concept-annotated dataset, a semantic evaluation metric, and query-conditioned personalized summarization. Its memory-network and determinantal-point-process model uses attention and diversity modeling, while experiments support the model and metric.
- Contributions: The paper compiles a densely concept-annotated dataset and designs an evaluation metric based on the collected annotations.The metric is intended to address semantic evaluation under subjective summary preferences.
- Contributions: The proposed summarizer incorporates user queries through memory networks and uses determinantal point processes to model summary diversity.The conclusion presents these mechanisms as the basis for personalized summaries.
- Findings: Extensive experiments support the effectiveness of the summarization approach and reveal favorable behavior of the evaluation metric.The conclusion summarizes both the modeling and evaluation findings.