Source-linked AI summary
VideoSET: Video Summary Evaluation through Text
Serena Yeung, Alireza Fathi, Li Fei-Fei
TL;DR
VideoSET addresses the absence of a standard, efficient way to evaluate whether video summaries preserve semantic information. It converts summaries into text, compares them with human-written ground-truth summaries using NLP metrics, and reports stronger agreement with human judgment than pixel-based distances while releasing supporting annotations and summaries.
Problem
Video-summary evaluation lacks a standard, automated, replicable method because user studies are difficult to replicate and pixel distance does not adequately measure semantic similarity.
Method
VideoSET converts a video summary into a text representation and compares it with multiple human-written ground-truth summaries using NLP-based semantic similarity.
Results
VideoSET correlates well with human judgment and outperforms pixel-based distance measures; ROUGE-SU has the strongest correlation among tested ROUGE metrics.
Takeaways & Limitations
Text-based comparison provides an automated, efficient, replicable way to evaluate semantic information retained by video summaries.
Abstract
from arXiv · showhide
In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.
1 Introduction
VideoSET addresses the lack of a standard, automated evaluation method for video summaries by measuring semantic similarity in the text domain rather than through pixels. It converts summaries into text and compares them with human-written ground-truth summaries.
- 1 Introduction: Video summarization lacks a standard, efficient evaluation method because user studies are difficult to replicate and pixel distance poorly captures semantic similarity.Existing criteria are varied, while visual distance can force semantically unrelated subshots to appear similar or equivalent semantic content to appear different.
- 1 Introduction: VideoSET targets an evaluation framework that measures distance from an ideal summary, operates automatically without human involvement, and supports standardized comparisons.These properties are presented as requirements for comparing summarization methods across datasets.
- 1 Introduction: VideoSET evaluates retained semantic information by converting a video summary into text and comparing it with human-written ground-truth summaries using NLP similarity measures.The approach is motivated by the observation that semantic similarity is most naturally expressed and measured through words.
- 1 Introduction: VideoSET provides a replicable, efficient, automated alternative to user studies for evaluating video summaries.Its stated benefits include measuring distance from an ideal and requiring no human involvement during evaluation.
- 1 Introduction: Text-distance comparisons better indicate semantic similarity than pixel-based comparisons because visually diverse subshots can convey the same information.The paper treats an informative summary maximizing semantic information as a useful target for evaluation.
2 Previous Work
Previous video-summarization work selects content using low-level visual features, objects, or interactions, while evaluation commonly relies on user studies or pixel comparisons. NLP summary evaluation offers a text-based precedent through ROUGE.
- Methods for video summarization: Video-summarization methods have used color, motion, object presence, and object interactions to select key subshots.Representative approaches combine low-level features or model objects and people to identify important events.
- Techniques for evaluating video summaries: Most prior video-summary evaluations use user studies, which require repeated comparisons after parameter changes and are difficult for others to replicate.Other methods evaluate object presence or compare summaries with the original video using pixels.
- Techniques for evaluating video summaries: Pixel-based evaluation does not necessarily measure semantic similarity because it emphasizes color and texture similarity between subshots.This limitation motivates moving evaluation toward textual semantic content.
- Techniques for evaluating text summaries: NLP has established text-summary evaluation methods, including ROUGE, whose algorithms were inspired by machine-translation evaluation.This prior progress provides the textual evaluation basis used by VideoSET.
3 Evaluation Framework
The VideoSET framework represents a summary as text derived from annotations of its selected subshots, compares that representation with human-written ground-truth summaries, and returns a semantic score. The authors also release the annotations and summaries needed to apply the framework.
- 3 Evaluation Framework: VideoSET accepts a subset of video subshots, generates its text representation from original-video annotations, and compares it with human-written ground-truth summaries.The ground-truth summaries specify the ideal semantic content of a video summary.
- 3 Evaluation Framework: The framework returns an evaluation score after comparing summary text with ground-truth text using an NLP-based content-similarity function.The scoring function uses a semantic similarity measure S(x, y), instantiated with ROUGE.
- 3 Evaluation Framework: VideoSET releases the text annotations and ground-truth summaries required to evaluate summaries across multiple video datasets.These resources make the framework applicable beyond a single experimental collection.
3.2 Datasets
VideoSET releases annotations and ground-truth summaries for two egocentric datasets and four television episodes. The release covers 40 hours across 11 videos, supporting evaluation on varied video content.
- 3.2 Datasets: The released resources cover two publicly available egocentric datasets and four TV episodes, with annotations and ground-truth summaries for the listed videos.The TV set contains one Castle episode, one The Mentalist episode, and two Numb3rs episodes.
- 3.2 Datasets: The Daily life egocentric dataset contains four 3–5-hour videos of natural activities such as eating, shopping, and cooking.Text annotations and ground-truth summaries are provided for all videos in this dataset.
- 3.2 Datasets: The Disneyworld egocentric dataset contains eight 6–8-hour videos, with annotations and ground-truth summaries provided for three videos.The videos record a subject during a day at Disneyworld Park.
- 3.2 Datasets: Overall, VideoSET provides annotations for 40 hours of data split across 11 videos.The annotations may also support research at the intersection of images or video and text.
3.3 Obtaining text annotations
VideoSET obtains short, factual text descriptions for fixed-length video subshots, using different perspective and tense instructions for egocentric videos and TV episodes.
- Egocentric videos were segmented into 5-second subshots and TV episodes into 10-second subshots before annotation.
- Mechanical Turk workers wrote one simple, factual sentence describing what happened in each subshot.
- Annotators used first-person past tense for egocentric videos and third-person present tense for TV episodes.
- Additional workers edited TV descriptions for vocabulary and grammatical consistency, while TV annotators were required to know the episode and use character names.
- Subshot lengths were selected after comparing annotations across candidate durations with ROUGE, which showed high similarity across lengths.
Choosing subshot length
VideoSET can evaluate keyframe and variable-length summaries by mapping them to annotations from short subshots that express single semantic concepts or events.
- Keyframe summaries can use the annotation for the subshot containing each keyframe, while variable-length summaries can also be mapped for evaluation.
- The mapping is appropriate because the subshots are short enough to express a single semantic concept or event.
3.4 Obtaining ground-truth summaries
Ground-truth summaries are collected as ranked text sentences, allowing humans to express semantic content without selecting visually identical subshots.
- Humans write semantic ground-truth summaries in words because information is easier to express than by selecting ideal subshots from long videos.
- Different workers can produce equally good ground-truth summaries, as illustrated by two alternative summaries for the same video.
- Workers receive video annotations, write simple sentences with similar content level, and rank the sentences by importance.
- Workers typically produced 40-60 ranked sentences per egocentric video and 20-30 per TV episode.
3.5 Generating the text representation of a video summary
VideoSET represents a selected video summary by concatenating its existing subshot annotations, then scores the resulting text against human summaries with NLP similarity measures.
- A summary’s text representation is generated by concatenating the pre-existing annotations associated with its selected subshots.
- ROUGE-SU compares the candidate representation with ground-truth text using unigram and skip-bigram co-occurrence after stemming and stopword removal.
- ROUGE-SU had the strongest correlation with human judgment among the ROUGE metrics tested.
- To accommodate semantic subjectivity, VideoSET scores a summary against multiple ground-truth summaries using the maximum pairwise score.
4 Experiments
Experiments compare VideoSET with pixel-based evaluation against human judgments using existing-method summaries and randomly sampled summaries and subshots. VideoSET correlates better with human semantic judgments, especially when visual and semantic similarity diverge.
- 4.1 VideoSET evaluation of existing summarization methods: The first experiment evaluates 2-minute summaries generated by uniform sampling, color-histogram clustering, Video-MMR, object-driven summarization, and text-guided methods.Summaries were produced for egocentric videos and TV episodes, then ranked using VideoSET, pixel distance, and human judgments.
- 4.1 VideoSET evaluation of existing summarization methods: Human rankings assessed semantic similarity to ground-truth written summaries, while automated rankings used the highest score across three ground-truth summaries.The comparison used Spearman rank correlation between automated metrics and human-assigned ranks.
- 4.1 VideoSET evaluation of existing summarization methods: VideoSET strongly correlates with human judgment and outperforms pixel-based distance across existing-method summaries.The largest correlation gap occurs on Disney videos, while both methods perform strongly on highly edited TV episodes.
- 4.2 VideoSET Evaluation of Randomly Sampled Summaries and Subshots: Among 100 random pairs of 2-minute summaries, humans disagreed on 40% of comparisons, and VideoSET showed higher agreement than pixel distance on the remaining cases.The experiment compared which summary was semantically closer to a provided ground-truth text summary.
- 4.2 VideoSET Evaluation of Randomly Sampled Summaries and Subshots: For subshot comparisons, VideoSET agreed with human judgment more strongly than pixel distance, and agreed more than twice as often when the metrics otherwise disagreed.Most comparisons were judged to have zero similarity: 65.1% by VideoSET and 77.3% by humans.
5 Conclusion
The conclusion presents VideoSET as an automatic text-based evaluation technique for measuring semantic retention in video summaries. Experiments show strong correlation with human judgment and better performance than pixel-based measures, while the framework supports broader summary types and future metric extensions.
- 5 Conclusion: VideoSET converts video summaries into text and measures their semantic distance from human-written ground-truth summaries.The technique automatically evaluates how well a summary retains semantic information from the original video.
- 5 Conclusion: Experiments show that VideoSET correlates well with human judgment and outperforms pixel-based distance measures.
- 5 Conclusion: The framework can be extended to evaluate any type of video summary and accommodate future extensions to its semantic distance metric.