Proceedings of the Workshop on New Frontiers in Summarization
Bibliographic record
Abstract
With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection.Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection.However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective.The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lagcalibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping.Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.𝜏 67889:; for number of comments in each highlight summary.Our task is to (1) generate a set of highlight shots 𝑆(𝒗) = {𝑠 % , 𝑠 ' , 𝑠 ( , … , 𝑠 @ }, and (2) highlight summaries Α 𝒗 = {𝐼 % , 𝐼 ' , 𝐼 ( , … , 𝐼 @ } as close to ground truth as possible.Each highlight summary comprises a subset of all the comments in this shot: 𝐼 2 = {𝑐 % , 𝑐 ' , 𝑐 ( , … , 𝑐 @ C }. Number of highlight shots 𝑛 and number of comments in summary 𝑛 2 are determined by 𝜏 123142315 and 𝜏 67889:; respectively. Video Highlight DetectionIn this section, we introduce our framework for highlight detection.Two preliminary tasks are also described, namely construction of global timesync comment word embedding and emotion lexicon. 4.1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".