Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged. The proposed method thus holds promise for producing grammatical sentences and encouraging the summary to stay true-to-original. Our contributions of this work are twofold. First, we present a novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse. Secondly, we describe a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings. We evaluate our method on a number of summarization datasets and demonstrate competitive results against strong baselines.

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer’s performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.

The most important obstacles facing multidocument summarization include excessive redundancy in source descriptions and the looming shortage of training data.These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well.In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks.The approach measures redundancy between a pair of sentences based on surface form and semantic information.We show that our DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets.Our findings are particularly meaningful for summarizing documents created by multiple authors containing redundant yet lexically diverse expressions. 1

When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence.However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood.Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them.There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space.Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence.We conduct extensive experiments on both single-and multidocument summarization datasets and report findings on sentence selection and abstraction.

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.

Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies.However, privacy policies are often vague and people struggle to understand the content.Their opaqueness poses a significant challenge to both users and policy regulators.In this paper, we seek to identify vague content in privacy policies.We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection.In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness.Our experimental results demonstrate the effectiveness of proposed approaches.Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.

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.