Articles

We restate the classical logical notion of generation/parsing reversibility in terms of feasible probabilistic sampling, and argue for an implementation based on finite-state factors. We propose a modular decompo- sition that reconciles generation accuracy with parsing robustness and allows the in- troduction of dynamic contextual factors. (Opinion Piece) 1...

In this study, we consider a summariza- tion method using the document level sim- ilarity based on embeddings, or distributed representations of words, where we as- sume that an embedding of each word can represent its “meaning.” We formalize our task as the problem of maximizing a sub- modular function …...

While it has been established that transi- tions between discourse relations are im- portant for coherence, such information has not so far been used to aid in lan- guage generation. We introduce an ap- proach to discourse planning for concept- to-text generation systems which simul- taneously determines the order of …...

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this pa- per, we introduce a large corpus of Chi- nese short text …...

We propose to automatically summarize student responses to reflection prompts and introduce a novel summarization algo- rithm that differs from traditional methods in several ways. First, since the linguis- tic units of student inputs range from sin- gle words to multiple sentences, our sum- maries are created from extracted phrases …...

We present an approach for extractive single-document summarization. Our ap- proach is based on a weighted graphical representation of documents obtained by topic modeling. We optimize importance, coherence and non-redundancy simulta- neously using ILP. We compare ROUGE scores of our system with state-of-the-art results on scientific articles from PLOS Medicine …...

We present experiments with generative models for linearization of unordered la- beled syntactic dependency trees (Belz et al., 2011; Rajkumar and White, 2014). Our linearization models are derived from generative models for dependency struc- ture (Eisner, 1996). We present a series of generative dependency models designed to capture successively more …...

The most successful approaches to extrac- tive text summarization seek to maximize bigram coverage subject to a budget con- straint. In this work, we propose instead to maximize semantic volume. We em- bed each sentence in a semantic space and construct a summary by choosing a sub- set of sentences …...

Storyline detection from news articles aims at summarizing events described un- der a certain news topic and revealing how those events evolve over time. It is a dif- ficult task because it requires first the de- tection of events from news articles pub- lished in different time periods and then …...

Automated text summarization is aimed at extracting essential information from orig- inal text and presenting it in a minimal, often predefined, number of words. In this paper, we introduce a new approach for unsupervised extractive summariza- tion, based on the Minimum Description Length (MDL) principle, using the Krimp dataset compression …...

ROUGE is a widely adopted, automatic evaluation measure for text summariza- tion. While it has been shown to corre- late well with human judgements, it is bi- ased towards surface lexical similarities. This makes it unsuitable for the evalua- tion of abstractive summarization, or sum- maries with substantial paraphrasing. We …...

This paper demonstrates the effectiveness of a Long Short-Term Memory language model in our initial efforts to generate un- constrained rap lyrics. The goal of this model is to generate lyrics that are simi- lar in style to that of a given rapper, but not identical to existing lyrics: this …...

In concept-based summarization, sentence selection is modelled as a budgeted maxi- mum coverage problem. As this problem is NP-hard, pruning low-weight concepts is required for the solver to find optimal solutions efficiently. This work shows that reducing the number of concepts in the model leads to lower ROUGE scores, and …...

ive Multi-document Summarization with Semantic Infor- mation Extraction Wei Li Key Lab of Intelligent Info. Processing, Institute of Computing Technology, CAS Beijing, 100190, China weili@kg.ict.ac.cn Abstract This paper proposes a novel approach to generate abstractive summary for multi- ple documents by extracting semantic in- formation from texts. The concept of …...

Predicting the success of referring expres- sions (RE) is vital for real-world applica- tions such as navigation systems. Tradi- tionally, research has focused on studying Referring Expression Generation (REG) in virtual, controlled environments. In this paper, we describe a novel study of spa- tial references from real scenes rather than …...

Speech translation is conventionally car- ried out by cascading an automatic speech recognition (ASR) and a statistical ma- chine translation (SMT) system. The hy- potheses chosen for translation are based on the ASR system’s acoustic and lan- guage model scores, and typically opti- mized for word error rate, ignoring the …...

This paper focuses on language modeling with adequate robustness to support differ- ent domain tasks. To this end, we propose a hierarchical latent word language model (h-LWLM). The proposed model can be regarded as a generalized form of the stan- dard LWLMs. The key advance is in- troducing a multiple …...

We describe an approach to create a di- verse set of predictions with spectral learn- ing of latent-variable PCFGs (L-PCFGs). Our approach works by creating multiple spectral models where noise is added to the underlying features in the training set before the estimation of each model. We describe three ways …...

Recently, neural network based depen- dency parsing has attracted much interest, which can effectively alleviate the prob- lems of data sparsity and feature engineer- ing by using the dense features. How- ever, it is still a challenge problem to sufficiently model the complicated syn- tactic and semantic compositions of the …...

Accurate multilingual transfer parsing typ- ically relies on careful feature engineer- ing. In this paper, we propose a hierar- chical tensor-based approach for this task. This approach induces a compact feature representation by combining atomic fea- tures. However, unlike traditional tensor models, it enables us to incorporate prior knowledge about …...