Centering Theory in natural text: a large-scale corpus study

We present an extensive corpus study of Centering Theory (CT), examining how adequately CT models coherence in a large body of natural text. A novel analysis of transition bigrams provides strong empirical support for several CT-related linguistic claims which so far have been investigated only on various small data sets. The study also reveals genre-based differences in texts’ degrees of entity coherence. Previous work has shown unsupervised CT-based coherence metrics to be unable to outperform a simple baseline. We identify two reasons: 1) these metrics assume that some transition types are more coherent and that they occur more frequently than others, but in our corpus the latter is not the case; and 2) the original sentence order of a document and a random permutation of its sentences differ mostly in the fraction of entity-sharing sentence pairs, exactly the factor measured by the baseline

Graph-based Neural Multi-Document Summarization

We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences while avoiding redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.Comment: In CoNLL 201

Multi-document text summarization using text clustering for Arabic Language

The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text summarization is redundancy of sentences, and thus, redundancy must be eliminated to ensure coherence, and improve readability. Hence, we set out the main objective as to examine multi-document summarization salient information for text Arabic summarization task with noisy and redundancy information. In this research we used Essex Arabic Summaries Corpus (EASC) as data to test and achieve our main objective and of course its subsequent subobjectives. We used the token process to split the original text into words, and then removed all the stop words, and then we extract the root of each word, and then represented the text as bag of words by TFIDF without the noisy information. In the second step we applied the K-means algorithm with cosine similarity in our experimental to select the best cluster based on cluster ordering by distance performance. We applied SVM to order the sentences after selected the best cluster, then we selected the highest weight sentences for the final summary to reduce redundancy information. Finally, the final summary results for the ten categories of related documents are evaluated using Recall and Precision with the best Recall achieved is 0.6 and Precision is 0.6

Centering theory in natural text: a large-scale corpus study

We present an extensive corpus study of Centering Theory (CT), examining how adequately CT models coherence in a large body of natural text. A novel analysis of transition bigrams provides strong empirical support for several CT-related linguistic claims which so far have been investigated only on various small data sets. The study also reveals genre-based differences in texts’ degrees of entity coherence. Previous work has shown unsupervised CTbased coherence metrics to be unable to outperform a simple baseline. We identify two reasons: 1) these metrics assume that some transition types are more coherent and that they occur more frequently than others, but in our corpus the latter is not the case; and 2) the original sentence order of a document and a random permutation of its sentences differ mostly in the fraction of entity-sharing sentence pairs, exactly the factor measured by the baseline