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A Natural Language Generation system produces text using as input semantic data. One of its very first tasks is to decide which pieces of information to convey in the output. …...

In this paper, we explore the use of automatic syntactic simplification for improving content selection in multi-document summarization. In particular, we show how simplifying parenthet- icals by removing relative clauses …...

A content selection component deter- mines which information should be con- veyed in the output of a natural language generation system. We present an effi- cient method for automatically learning …...

We describe a probabilistic approach to content se- lection for meeting summarization. We use skip- chain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as …...

We examine the problem of content selection in statistical novel sentence generation. Our approach models the processes performed by professional editors when incorporating ma- terial from additional sentences to support …...

Document Planning - the task of deciding which content messages should be realised in a target document based on raw data provided by an underlying application, and how these messages …...

Discourse theories claim that text gets meaning in context. Most summarization systems do not take advantage of this. They assess the rele- vance of each passage individually rather than modeling …...

We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into …...

In this paper we describe machine learn- ing experiments that aim to characterise the content selection process for distin- guishing descriptions. Our experiments are based on two large corpora of …...

We present a novel approach for automatic report generation from time-series data, in the context of student feedback genera- tion. Our proposed methodology treats content selection as a multi-label (ML) …...

In this paper, we present a study on per- sonalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individ- …...

This paper presents a generic approach to con- tent selection for creating timelines from in- dividual history articles for which no exter- nal information about the same topic is avail- …...

AbstractWe present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model …...

This paper describes the Metalogue Debate Trainee Corpus (DTC). DTC has been collected and annotated in order to facilitate the design of instructional and interactive models for Virtual Debate Coach …...

AbstractWe present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide a sufficient amount …...

AbstractSequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. …...

AbstractNeural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data. In this work, we propose …...

AbstractTable-to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems are still …...

AbstractTransformer models cannot easily scale to long sequences due to their O(Nˆ2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer …...