Multi-Document Summarization By Sentence Extraction

This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-i formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selec- tion are critical in the formation of useful summaries

The use of MMR, diversity-based reranking for reordering documents and producing summaries

jadeQcs.cmu.edu Abstract This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection. 2 Maximal Marginal Relevance Most modem IR search engines produce a ranked list of retrieved documents ordered by declining relevance to the user’s query. In contrast, we motivated the need for “relevant novelty ” as a potentially superior criterion. A first approximation to measuring relevant novelty is to measure relevance and novelty independently and provide a linear combination as the metric. We call the linear combination “marginal relevance ”- i.e. a document has high marginal relevance if it is both relevant to the query and contains minimal similarity to previously selected documents. We strive to maximize-marginal relevance in retrieval and summarization, hence we label our method “maximal marginal relevanci ” (MMR)

Creating and Evaluating Multi-Document Sentence Extract

This paper discusses passage extraction approaches to multidocument summarization that use available information about the document set as a whole and the relationships between the documents to build on single document summarization methodology. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries, as well as the user's goals in creating the summary. Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for different genres, corpora characteristics and user requirements. We examined how humans create multi-document summaries as well as the characteristics of such summaries and use these summaries to evaluate the performance of various multidocument summarization algorithms

Using Aggregation and Dynamic Queries for Exploring Large Data Sets

When working with large data sets, users perform three primary types of activities: data manipulation, data analysis, and data visualization. The data manipulation process involves the selection and transformation of data prior to viewing. This paper addresses user goals for this process and the interactive interface mechanisms that support them. We consider three classes of data manipulation goals: controlling the scope (selecting the desired portion of the data), selecting the focus of attention (concentrating on the attributes of data that are relevant to current analysis), and choosing the level of detail (creating and decomposing aggregates of data). We use this classification to evaluate the functionality of existing data exploration interface techniques. Based on these results, we have expanded an interface mechanism called the Aggregate Manipulator (AM) and combined it with Dynamic Query (DQ) to provide complete coverage of the data manipulation goals. We use real estate sales data to demonstrate how the AM and DQ synergistically function in our interface

The Use of MMR and Diversity-Based Reranking in Document Reranking and Summarization

This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection

Genre identification and goal-focused summarization

In this paper, we present a novel technique of first performing document genre identification, then utilizing the genre for producing tailored summaries based on a user’s information seeking needs – genre oriented goal-focused summarization – such as a plot or opinion summary of a movie review. We create a test corpus to determine genre classification accuracy for 16 genres, and examine performance on various amounts of training data for machine learning algorithms- Random Forests, SVM light and Naïve Bayes. Results show that Random Forests outperforms SVM light and Naïve Bayes. The genre tag is used to inform a downstream summarization engine. We define types of summaries for 7 genres, create a ground truth corpus and analyze the results of genre oriented goal-focused summarization, showing that this type of user based summarization requires different algorithms than the leading sentence baseline which is known to perform well in the case of news articles

Interactive Graphic Design Using Automatic Presentation Knowledge

We present three novel tools for creating data graphics: (1) SageBrush, for assembling graphics from primitive objects like bars, lines and axes, (2) SageBook, for browsing previously created graphics relevant to current needs, and (3) SAGE, a knowledge-based presentation system that automatically designs graphics and also interprets a user's specifications conveyed with the other tools. The combination of these tools supports two complementary processes in a single environment: design as a constructive process of selecting and arranging graphical elements, and design as a process of browsing and customizing previous cases. SAGE enhances userdirected design by completing partial specifications, by retrieving previously created graphics based on their appearance and data content, by creating the novel displays that users specify, and by designing alternatives when users request them. Our approach was to propose interfaces employing styles of interaction that appear to support graphic d..

Summarizing Text Documents: Sentence Selection and Evaluation Metrics

Human-quality text summarization systems are difficult to design, and even more difficult to evaluate, in part because documents can differ along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods. The potential linguistic ones were derived from an analysis of news-wire summaries. Toevaluate these features we use a normalized version of precision-recall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical results showing the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries