Automatic text summarization aims at producing a shorter version of the input text that conveys the most important information. However, multi-lingual text summarization, where the goal is to process texts in multiple languages and output summaries in the corresponding languages with a single model, has been rarely studied. In this paper, we present MultiSumm, a novel multi-lingual model for abstractive summarization. The MultiSumm model uses the following training regime: (I) multi-lingual learning that contains language model training, auto-encoder training, translation and back-translation training, and (II) joint summary generation training. We conduct experiments on summarization datasets for five rich-resource languages: English, Chinese, French, Spanish, and German, as well as two low-resource languages: Bosnian and Croatian. Experimental results show that our proposed model significantly outperforms a multi-lingual baseline model. Specifically, our model achieves comparable or even better performance than models trained separately on each language. As an additional contribution, we construct the first summarization dataset for Bosnian and Croatian, containing 177,406 and 204,748 samples, respectively.

Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries.We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units.Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient.These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.

In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer linear programming solvers. Our formulation suits for any objective function involving arbitrary local score definition. Experimental results show that the proposed method achieves nearly identical performance with explicit ILP formulation while being much more efficient.

We study the task of constructing sports news report automatically from live commentary and focus on content selection.Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging setting to generate news report on the fly by treating live text input as a stream.We design scoring functions to address different requirements of the task and use stream substitution for sentence selection.Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model.

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

In this paper, we investigate the possibility to automatically generate sports news from live text commentary scripts.As a preliminary study, we treat this task as a special kind of document summarization based on sentence extraction.We formulate the task in a supervised learning to rank framework, utilizing both traditional sentence features for generic document summarization and novelly designed task-specific features.To tackle the problem of local redundancy, we also propose a probabilistic sentence selection algorithm.Experiments on our collected data from football live commentary scripts and corresponding sports news demonstrate the feasibility of this task.Evaluation results show that our methods are indeed appropriate for this task, outperforming several baseline methods in different aspects.

Annotating images with tags is useful for indexing and retrieving images. However, many available annotation data include missing or inaccurate annotations. In this paper, we propose an image annotation framework which sequentially performs tag completion and refinement. We utilize the subspace property of data via sparse subspace clustering for tag completion. Then we propose a novel matrix completion model for tag refinement, integrating visual correlation, semantic correlation and the novelly studied property of complex errors. The proposed method outperforms the state-of-the-art approaches on multiple benchmark datasets even when they contain certain levels of annotation noise.

The task of cross-language document summarization is to create a summary in a target language from documents in a different source language.Previous methods only involve direct extraction of automatically translated sentences from the original documents.Inspired by phrasebased machine translation, we propose a phrase-based model to simultaneously perform sentence scoring, extraction and compression.We design a greedy algorithm to approximately optimize the score function.Experimental results show that our methods outperform the state-of-theart extractive systems while maintaining similar grammatical quality.

In this paper, we provide a new method for decoding tree transduction based sentence compression models augmented with lan-guage model scores, by jointly decoding two components. In our proposed so-lution, rich local discriminative features can be easily integrated without increasing computational complexity. Utilizing an unobvious fact that the resulted two com-ponents can be independently decoded, we conduct efficient joint decoding based on dual decomposition. Experimental results show that our method outperforms tradi-tional beam search decoding and achieves the state-of-the-art performance. 1