Opinion summarization aims to generate concise summaries that present popular
opinions of a large group of reviews. However, these summaries can be too
generic and lack supporting details. To address these issues, we propose a new
paradigm for summarizing reviews, rationale-based opinion summarization.
Rationale-based opinion summaries output the representative opinions as well as
one or more corresponding rationales. To extract good rationales, we define
four desirable properties: relatedness, specificity, popularity, and diversity
and present a Gibbs-sampling-based method to extract rationales. Overall, we
propose RATION, an unsupervised extractive system that has two components: an
Opinion Extractor (to extract representative opinions) and Rationales Extractor
(to extract corresponding rationales). We conduct automatic and human
evaluations to show that rationales extracted by RATION have the proposed
properties and its summaries are more useful than conventional summaries. The
implementation of our work is available at
this https URL

我们提出了一种基于理由的观点总结新方法，输出代表性观点及相应理由，通过 Gibbs 采样法提取优质理由，并实现了两个组件的无监督抽取系统 RATION，评估表明 RATION 的理由具有所提出的属性，其总结比传统总结更实用。

基于理性的观点摘要

Rationale-based Opinion Summarization

Legal contracts, such as employment or lease agreements, are important
documents as they govern the obligations and entitlements of the various
contracting parties. However, these documents are typically long and written in
legalese resulting in lots of manual hours spent in understanding them. In this
paper, we address the task of summarizing legal contracts for each of the
contracting parties, to enable faster reviewing and improved understanding of
them. Specifically, we collect a dataset consisting of pairwise importance
comparison annotations by legal experts for ~293K sentence pairs from lease
agreements. We propose a novel extractive summarization system to automatically
produce a summary consisting of the most important obligations, entitlements,
and prohibitions in a contract. It consists of two modules: (1) a content
categorize to identify sentences containing each of the categories (i.e.,
obligation, entitlement, and prohibition) for a party, and (2) an importance
ranker to compare the importance among sentences of each category for a party
to obtain a ranked list. The final summary is produced by selecting the most
important sentences of a category for each of the parties. We demonstrate the
effectiveness of our proposed system by comparing it against several text
ranking baselines via automatic and human evaluation.

本文提出了一种新的摘要提取系统，以自动选择契约中最重要的义务、权利和禁令等内容进行合同文本的摘要生成，通过将其与几种文本排名基准进行比较，通过自动和人工评估，证明了该系统的有效性。