Query-Focused Extractive Video Summarization
نویسندگان
چکیده
Section A presents the list of concepts used in our experiments for UTE and TV episodes datasets. Section B studies the effect of ground set size on the performance of our proposed method, SH-DPP. Section C presents the training and optimization details of SH-DPP. Section D studies the lower dimensions of the model parameters W and V. Section E shows a few system generated video summaries in full by SH-DPP.
منابع مشابه
Feature expansion for query-focused supervised sentence ranking
We present a supervised sentence ranking approach for use in extractive summarization. Using a general machine learning technique provides great flexibility for incorporating varied new features, which we demonstrate. The system proves quite effective at query-focused multi-document summarization, both for single summaries and for series of update summaries.
متن کاملA Unified Multi-Faceted Video Summarization System
T his paper addresses automatic summarization and search in visual data comprising of videos, live streams and image collections in a unified manner. In particular, we propose a framework for multi-faceted summarization which extracts keyframes (image summaries), skims (video summaries) and entity summaries (summarization at the level of entities like objects, scenes, humans and faces in the vi...
متن کاملQuery Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent developments in neural-attention based sequence-to-sequence models have led to state-of-the-art results on the task of abstractive generic sin...
متن کاملQuery-focused Supervised Sentence Ranking for Update Summaries
We present a supervised sentence ranking approach for use in extractive update summarization. We use the same general machine learning approach described in earlier DUC papers, and adapt it to the update summarization task. The system proves adaptable enough to be effective at queryfocused update summaries.
متن کاملAttSum: Joint Learning of Focusing and Summarization with Neural Attention
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel sum...
متن کامل