Collaborative Summarization of Topic-Related Videos

Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.Comment: CVPR 201

Abstractive Multi-Document Summarization via Phrase Selection and Merging

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201

Multi-Document Summarization using Distributed Bag-of-Words Model

As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time. In this paper, we present an unsupervised centroid-based document-level reconstruction framework using distributed bag of words model. Specifically, our approach selects summary sentences in order to minimize the reconstruction error between the summary and the documents. We apply sentence selection and beam search, to further improve the performance of our model. Experimental results on two different datasets show significant performance gains compared with the state-of-the-art baselines

Enumeration of Extractive Oracle Summaries

To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.Comment: 12 page