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

Keyword Merging Based Multi Document Enhanced Summarization

Automatic text summarization is a wide research area. There are several ways in which one can characterize different approaches to text summarization: extractive and abstractive from single document or multi document. Summary is text that is produced from one or more text. Document summarization is a procedure that building coated version of document that gives respected data to the client, and multi-document summarization is to produce a summary conveying the larger part of data substance from a set of documents about an implicit or explicit primary point.This paper describes a system for the summarization of multiple documents. The system produces multi-document summaries using data merging techniques. For combining multiple document on same thing the system uses Bisecting k-means algorithm which works better than basic K-means algorithm.Our System uses Enhanced Summarization algorithm to summarize multiple document.The Enhanced algorithm is applied separately on each cluster. According to results this system gives better results as compared to NEWSUM algorithm. DOI: 10.17762/ijritcc2321-8169.150711

Shaping Political Discourse using multi-source News Summarization

Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic. This summary can help users quickly understand the key information from a large collection of documents. Multi-document summarization systems are more complex than single-document summarization systems due to the need to identify and combine information from multiple sources. In this paper, we have developed a machine learning model that generates a concise summary of a topic from multiple news documents. The model is designed to be unbiased by sampling its input equally from all the different aspects of the topic, even if the majority of the news sources lean one way

Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature

Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in the medical domain where each information can be vital. Others leverage linear model approximations to apply multi-input concatenation, worsening the results because all information is considered, even if it is conflicting or noisy with respect to a shared background. Despite the importance and social impact of medicine, there are no ad-hoc solutions for multi-document summarization. For this reason, we propose a novel discriminative marginalized probabilistic method (DAMEN) trained to discriminate critical information from a cluster of topic-related medical documents and generate a multi-document summary via token probability marginalization. Results prove we outperform the previous state-of-the-art on a biomedical dataset for multi-document summarization of systematic literature reviews. Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method

Query-Based Summarization: A survey

This paper presents a survey of recent extractive query-based summarization techniques. We explore approaches for single document and multi-document summarization. Knowledge-based and machine learning methods for choosing the most relevant sentences from documents with respect to a given query are considered. Further, we expose tailored summarization techniques for particular domains like medical texts. The most recent developments in the field are presented with opinion summarization of blog entries.This research is supported by the SmartBook project, subsidized by the Bulgarian National Science Fund, under Grant D002-111 /15.12.2008

Single-Document and Multi-Document Summarization Techniques for Email Threads Using Sentence Compression First Author Affiliation / Address line 1

We present two approaches to email thread summarization: Collective Message Summarization (CMS) applies a multi-document summarization approach, while Individual Message Summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate our techniques on the Enron collection—a very challenging corpus because of the highly technical language. Results suggest that CMS represents a better approach and additional findings pave the way for future explorations.