A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.

Chen Li, Yang Liu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary.These concepts are often obtained by selecting bigrams from the documents.In this paper, we improve such bigram based ILP summarization methods from different aspects.First we use syntactic information to select more important bigrams.Second, to estimate the importance of the bigrams, in addition to the internal features based on the test documents (e.g., document frequency, bigram positions), we propose to extract features by leveraging multiple external resources (such as word embedding from additional corpus, Wikipedia, Dbpedia, Word-Net, SentiWordNet).The bigram weights are then trained discriminatively in a joint learning model that predicts the bigram weights and selects the summary sentences in the ILP framework at the same time.We demonstrate that our system consistently outperforms the prior ILP method on different TAC data sets, and performs competitively compared to other previously reported best results.We also conducted various analyses to show the contributions of different components.

Integer Linear Programming (ILP) based summarization methods have been widely adopted recently because of their state-of-the-art performance. This paper proposes two new modifications in this framework for update summarization. Our key idea is to use discriminative models with a set of features to measure both the salience and the novelty of words and sentences. First, these features are used in a supervised model to predict the weights of the concepts used in the ILP model. Second, we generate preliminary sentence candidates in the ILP model and then rerank them using sentence level features. We evaluate our method on different TAC update summarization data sets, and the results show that our system performs competitively compared to the best TAC systems based on the ROUGE evaluation metric.

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summarization.We propose an innovative sentence compression method by considering every node in the constituent parse tree and deciding its status -remove or retain.Integer liner programming with discriminative training is used to solve the problem.Under this model, we incorporate various constraints to improve the linguistic quality of the compressed sentences.Then we utilize a pipeline summarization framework where sentences are first compressed by our proposed compression model to obtain top-n candidates and then a sentence selection module is used to generate the final summary.Compared with state-ofthe-art algorithms, our model has similar ROUGE-2 scores but better linguistic quality on TAC data.

Joint compression and summarization has been used recently to generate high quality summaries.However, such word-based joint optimization is computationally expensive.In this paper we adopt the 'sentence compression + sentence selection' pipeline approach for compressive summarization, but propose to perform summary guided compression, rather than generic sentence-based compression.To create an annotated corpus, the human annotators were asked to compress sentences while explicitly given the important summary words in the sentences.Using this corpus, we train a supervised sentence compression model using a set of word-, syntax-, and documentlevel features.During summarization, we use multiple compressed sentences in the integer linear programming framework to select salient summary sentences.Our results on the TAC 2008 and 2011 summarization data sets show that by incorporating the guided sentence compression model, our summarization system can yield significant performance gain as compared to the state-of-the-art.

There are many abbreviation and non-standard words in SMS and Twitter messages. They are problematic for text-to-speech (TTS) or language processing techniques for these data. A character-based machine translation (MT) approach was previously used for normalization of non-standard words. In this paper, we propose a two-stage translation method to leverage phonetic information, where non-standard words are first translated to possible pronunciations, which are then translated to standard words. We further combine it with the single-step character-based translation module. Our experiments show that our proposed method significantly outperforms previous results in both n-best coverage and 1-best accuracy.