Extractive Financial Narrative Summarisation based on DPP s (2020)

Abstract: We participate in the FNS-Summarisation 2020 shared task to be held at FNP 2020 workshop at COLING 2020. Based on Determinantal Point Processes (DPPs), we build an extractive automatic financial summarisation system for the specific task. In this system, we first analyze the long report data to select the important narrative parts and generate an intermediate document. Next, we build the kernel Matrix L for the intermediate document, which represents the quality of its sentences. On the basis of L, we then can use the DPPs sampling algorithm to choose those sentences with high quality and diversity as the final summary sentences.

## introduction with the development and progress of the economy, the popularity of the financial sector is increasing day by day. there are quite a few new companies gradually emerging and going public, but investors often find it difficult to deal with the long-form annual reports of various companies, because their content may be tedious and redundant, and it is difficult to filter out effective key information by human resources, so an automatic digest system is needed to help investors effectively screen company information. in this paper, we try to solve the first task contained in fns (financial narrative summarisation) 2020, the dataset we use is the annual reports in the financial field provided by the organizer. as there is much redundant information in the reports, we plan to select the most useful parts first as the intermediate documents to be summarized. for the shared task of summarisation, we implement a quality-diversity model (qd) based on determinantal point processes (dpps) to represent the intermediate document, and merge three kinds of features to rank the sentences. for the fns 2020 shared task we use 3863 uk annual reports for firms listed on the london stock exchange (lse) covering the period between 2002 and 2017. uk annual reports are lengthy documents this work is licensed under a creative commons attribution 4.0 international license. license details: http: //creativecommons.org/licenses/by/4.0/ . with around 80 pages on average, some annual reports could span over more than 250 pages. these uk annual reports are divided into training, testing and validation sets. the training and validation sets include the full text of each annual report along with the gold-standard summaries. on average there are at least 3 gold-standard summaries for each annual report with some reports containing up to 7 goldstandard summaries. for testing dataset, the task participants were given access only to the full texts. ## system in our system, we present an original quality-diversity model for extractive automatic summarisation based on dpps sampling algorithm @xcite . in this model, a single input document can be regarded as a set of sentences, and the process of extracting abstracts can be regarded as sampling a subset of important sentences from that set. to choose the high-quality and diversity summaries with dpps, we need to construct the kernel matrix l to represent the document. the main steps of summary generation include pre-processing, feature selection and sentence sampling. pre-processing, feature selection and sentence sampling are as followed. ## results in the shared task, we submitted a total of three versions of our system. cist-bupt-run3 uses sc as the feature that represents document, cist-bupt-run2 uses sl, while cist-bupt-run1 merges all three features: sc, sl and sp. we tried three versions on the pre-processed training set and as is shown in ## conclusion and future work actually, there are multiple ways to represent the document, one typical way is to train a sentence2vec model using neural network, which may improve the performance of the model. but because of the covid-19, the servers available are limited, we don't have enough computing resources to train the model. in the future, we'll try other feasible methods of document representation, seeing if they can help improve the quality of summaries.