Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization

In Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers above those posed by single-document summarization (SDS). These challenges include the increased search space and greater potential for the inclusion of redundant information. While advancements in deep learning approaches have led to the development of several advanced language models capable of summarization, the variety of training data specific to the problem of MDS remains relatively limited. Therefore, MDS approaches which require little to no pretraining, known as few-shot or zero-shot applications, respectively, could be beneficial additions to the current set of tools available in summarization. To explore one possible approach, we devise a strategy for combining state-of-the-art models' outputs using maximal marginal relevance (MMR) with a focus on query relevance rather than document diversity. Our MMR-based approach shows improvement over some aspects of the current state-of-the-art results in both few-shot and zero-shot MDS applications while maintaining a state-of-the-art standard of output by all available metrics

A reinforcement learning formulation to the complex question answering problem

International audienceWe use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. Given a set of complex questions, a list of relevant documents per question, and the corresponding human generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to previously unseen complex questions. A reward function is used to measure the similarities between the candidate (machine generated) summary sentences and the abstract summaries. In the training stage, the learner iteratively selects the important document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights are used to generate summaries as answers to unseen complex questions in the testing stage. Evaluation results show the effectiveness of our system. We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework

L'utilisation des POMDP pour les résumés multi-documents orientés par une thématique

National audienceL’objectif principal du résumé multi-documents orienté par une thématique est de générer un résumé à partir de documents sources en réponse à une requête formulée par l’utilisateur. Cette tâche est difficile car il n’existe pas de méthode efficace pour mesurer la satisfaction de l’utilisateur. Cela introduit ainsi une incertitude dans le processus de génération de résumé. Dans cet article, nous proposons une modélisation de l’incertitude en formulant notre système de résumé comme un processus de décision markovien partiellement observables (POMDP) car dans de nombreux domaines on a montré que les POMDP permettent de gérer efficacement les incertitudes. Des expériences approfondies sur les jeux de données du banc d’essai DUC ont démontré l’efficacité de notre approche

Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to reduce computational time and cost. In this paper, we tried to omit the fine-tuning steps and investigate whether the Marginal Maximum Relevance (MMR)-based approach can help the pre-trained models to obtain query-focused summaries directly from a new data set that was not used to pre-train the models. First, we used topic modelling on Wikipedia Current Events Portal (WCEP) and Debatepedia datasets to generate queries for summarization tasks. Then, using MMR, we ranked the sentences of the documents according to the queries. Next, we passed the ranked sentences to seven transformer-based pre-trained models to perform the summarization tasks. Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary. As indicated by the experimental results, our MMR-based approach successfully ranked and selected the most relevant sentences as summaries and showed better performance than the individual pre-trained models

Querybiased text summarization as a question-answering technique

Abstract (these are discussed in Section 3). Segments with strong Text summarization may soon become a competitive method of answering queries asked of large text corpora. A query brings up a set of documents. These documents are then filtered by a summarizer: it constructs brief summaries from document fragments conceptually close to the query terms. We present the implementation of such a summarization system, based on lexical semantics, and discuss its operation. It is a configurable system, in the sense that the user will be able to choose among two or more implementations of every major step