We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local co...

One goal of natural language generation is to produce coherent text that presents information in a logical order. In this paper, we show that topological fields, which model high-level clausal structure, are an important component of local coherence in German. First, we show in a sentence ordering experiment that topological field information improves the entity grid model of Barzilay and Lapat...

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 entit...

We describe cohere, our coherence toolkit which incorporates various complementary models for capturing and measuring different aspects of text coherence. In addition to the traditional entity grid model (Lapata, 2005) and graph-based metric (Guinaudeau and Strube, 2013), we provide an implementation of a state-of-the-art syntax-based model (Louis and Nenkova, 2012), as well as an adaptation of...

Entity resolution is the task of linking each mention of an entity in text to the corresponding record in a knowledge base (KB). Coherence models for entity resolution encourage all referring expressions in a document to resolve to entities that are related in the KB. We explore attentionlike mechanisms for coherence, where the evidence for each candidate is based on a small set of strong relat...

This paper presents our system for the “Making Sense of Microposts 2014 (#Microposts2014)” challenge. Our system is based on AIDA, an existing system that links entity mentions in natural language text to their corresponding canonical entities in a knowledge base (KB). AIDA collectively exploits the prominence of entities, contextual similarities, and coherence to effectively disambiguate entit...

Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of se...

We propose a simple and effective metric for automatically evaluating discourse coherence of a text using the outputs of a coreference resolution model. According to the idea that a writer tends to appropriately utilise coreference relations when writing a coherent text, we introduce a metric of discourse coherence based on automatically identified coreference relations. We empirically evaluate...

The intensive construction of domain-specific knowledge bases (DSKB) has posed an urgent demand for researches about domain-specific entity detection and linking (DSEDL). Joint models are usually adopted in DSEDL tasks, but data imbalance and high computational complexity exist in these models. Besides, traditional feature representation methods are insufficient for domain-specific tasks, due t...

Entity disambiguation works by linking ambiguous mentions in text to their corresponding real-world entities in knowledge base. Recent collective disambiguation methods enforce coherence among contextual decisions at the cost of non-trivial inference processes. We propose a fast collective disambiguation approach based on stacking. First, we train a local predictor g0 with learning to rank as b...