Cross-Language Text Summarization using Sentence and Multi-Sentence Compression

long paperInternational audienceCross-Language Automatic Text Summarization produces a summary in a language different from the language of the source documents. In this paper, we propose a French-to-English cross-lingual sum-marization framework that analyzes the information in both languages to identify the most relevant sentences. In order to generate more informative cross-lingual summaries, we introduce the use of chunks and two compression methods at the sentence and multi-sentence levels. Experimental results on the MultiLing 2011 dataset show that our framework improves the results obtained by state-of-the art approaches according to ROUGE metrics

Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks

Automatic summarization is the distillation of important information from a source into an abridged form for a particular user or task. Many current systems summarize texts by selecting sentences with important content. The limitation of extraction at the sentence level is that highly relevant sentences may also contain non-relevant and redundant content. This thesis presents a novel framework for text summarization that addresses the limitations of sentence-level extraction. Under this framework text summarization is performed by generating Multiple Alternative Sentence Compressions (MASC) as candidate summary components and using weighted features of the candidates to construct summaries from them. Sentence compression is the rewriting of a sentence in a shorter form. This framework provides an environment in which hypotheses about summarization techniques can be tested. Three approaches to sentence compression were developed under this framework. The first approach, HMM Hedge, uses the Noisy Channel Model to calculate the most likely compressions of a sentence. The second approach, Trimmer, uses syntactic trimming rules that are linguistically motivated by Headlinese, a form of compressed English associated with newspaper headlines. The third approach, Topiary, is a combination of fluent text with topic terms. The MASC framework for automatic text summarization has been applied to the tasks of headline generation and multi-document summarization, and has been used for initial work in summarization of novel genres and applications, including broadcast news, email threads, cross-language, and structured queries. The framework supports combinations of component techniques, fostering collaboration between development teams. Three results will be demonstrated under the MASC framework. The first is that an extractive summarization system can produce better summaries by automatically selecting from a pool of compressed sentence candidates than by automatically selecting from unaltered source sentences. The second result is that sentence selectors can construct better summaries from pools of compressed candidates when they make use of larger candidate feature sets. The third result is that for the task of Headline Generation, a combination of topic terms and compressed sentences performs better then either approach alone. Experimental evidence supports all three results

A Multilingual Study of Compressive Cross-Language Text Summarization

Cross-Language Text Summarization (CLTS) generates summaries in a language different from the language of the source documents. Recent methods use information from both languages to generate summaries with the most informative sentences. However, these methods have performance that can vary according to languages, which can reduce the quality of summaries. In this paper, we propose a compressive framework to generate cross-language summaries. In order to analyze performance and especially stability, we tested our system and extractive baselines on a dataset available in four languages (English, French, Portuguese, and Spanish) to generate English and French summaries. An automatic evaluation showed that our method outperformed extractive state-of-art CLTS methods with better and more stable ROUGE scores for all languages

Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.Comment: 11 page

A Novel ILP Framework for Summarizing Content with High Lexical Variety

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.Comment: Accepted for publication in the journal of Natural Language Engineering, 201