We applied a single-document sentencetrimming approach (Trimmer) to the problem of multi-document summarization. Trimmer was designed with the intention of compressing a lead sentence into a space consisting of tens of characters. In our Multi-Document Trimmer (MDT), we use Trimmer to generate multiple trimmed candidates for each sentence. Sentence selection is used to determine which trimmed candidates provide the best combination of topic coverage and brevity. We demonstrate that we were able to port Trimmer easily to this new problem. We also show that MDT generally ranked higher for recall than for precision, suggesting that MDT is currently more successful at finding relevant content than it is at weeding out irrelevant content. Finally, we present an error analysis that shows that, while sentence compressions is making space for additional sentences, more work is needed in the area of generating and selecting the right candidates. 1

We implemented an initial application of a sentence-trimming approach (Trimmer) to the problem of multi-document summarization in the MSE2005 and DUC2005 tasks. Sentence trimming was incorporated into a feature-based summarization system, called Multi-Document Trimmer (MDT), by using sentence trimming as both a preprocessing stage and a feature for sentence ranking. We demonstrate that we were able to port Trimmer easily to this new problem. Although the direct impact of sentence trimming was minimal compared to other features used in the system, the interaction of the other features resulted in trimmed sentences accounting for nearly half of the selected summary sentences. 1

In this paper we tackle the problem of automatic caption generation for news images. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned. Inspired by recent work in summarization, we propose extractive and abstractive caption generation models. They both operate over the output of a probabilistic image annotation model that preprocesses the pictures and suggests keywords to describe their content. Experimental results show that an abstractive model defined over phrases is superior to extractive methods. 1

We propose WIDL-expressions as a flexible formalism that facilitates the integration of a generic sentence realization system within end-to-end language processing applications. WIDL-expressions represent compactly probability distributions over finite sets of candidate realizations, and have optimal algorithms for realization via interpolation with language model probability distributions. We show the effectiveness of a WIDL-based NLG system in two sentence realization tasks: automatic translation and headline generation. 1

The task of selecting information and rendering it appropriately appears in multiple contexts in summarization. In this paper we present a model that simultaneously optimizes selection and rendering preferences. The model operates over a phrase-based representation of the source document which we obtain by merging PCFG parse trees and dependency graphs. Selection preferences for individual phrases are learned discriminatively, while a quasi-synchronous grammar (Smith and Eisner, 2006) captures rendering preferences such as paraphrases and compressions. Based on an integer linear programming formulation, the model learns to generate summaries that satisfy both types of preferences, while ensuring that length, topic coverage and grammar constraints are met. Experiments on headline and image caption generation show that our method obtains state-of-the-art performance using essentially the same model for both tasks without any major modifications. 1

This paper reports our results at DUC2004 and describes our approach, implemented in a system called Topiary.

We describe how components from two distinct multi-document summarization systems were combined. Twenty four possible combinations of components were considered. We observed some contrasts between conservative and aggressive sentence compression (i.e., trimming) in the context of multidocument summarization. 1