Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Better Summarization Evaluation with Word Embeddings for ROUGE

ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.Comment: Pre-print - To appear in proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP

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

ATSSI: Abstractive Text Summarization Using Sentiment Infusion

AbstractText Summarization is condensing of text such that, redundant data are removed and important information is extracted and represented in the shortest way possible. With the explosion of the abundant data present on social media, it has become important to analyze this text for seeking information and use it for the advantage of various applications and people. From past few years, this task of automatic summarization has stirred the interest among communities of Natural Language Processing and Text Mining, especially when it comes to opinion summarization. Opinions play a pivotal role in decision making in the society. Other's opinions and suggestions are the base for an individual or a company while making decisions. In this paper, we propose a graph based technique that generates summaries of redundant opinions and uses sentiment analysis to combine the statements. The summaries thus generated are abstraction based summaries and are well formed to convey the gist of the text