Template-aware Attention Model for Earnings Call Report Generation

Earning calls are among important resources for investors and analysts for updating their price targets. Firms usually publish corresponding transcripts soon after earnings events. However, raw transcripts are often too long and miss the coherent structure. To enhance the clarity, analysts write well-structured reports for some important earnings call events by analyzing them, requiring time and effort. In this paper, we propose TATSum (Template-Aware aTtention model for Summarization), a generalized neural summarization approach for structured report generation, and evaluate its performance in the earnings call domain. We build a large corpus with thousands of transcripts and reports using historical earnings events. We first generate a candidate set of reports from the corpus as potential soft templates which do not impose actual rules on the output. Then, we employ an encoder model with margin-ranking loss to rank the candidate set and select the best quality template. Finally, the transcript and the selected soft template are used as input in a seq2seq framework for report generation. Empirical results on the earnings call dataset show that our model significantly outperforms state-of-the-art models in terms of informativeness and structure.

Template Filling with Generative Transformers

Template filling is generally tackled by a pipeline of two separate supervised systems -- one for role-filler extraction and another for template/event recognition. Since pipelines consider events in isolation, they can suffer from error propagation. We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT). It naturally models the dependence between entities both within a single event and across the multiple events described in a document. Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies. We further show that our framework specifically improves performance on documents containing multiple events.

Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism

In this work, we describe our efforts in improving the variety of language generated from a rule-based NLG system for automated journalism. We present two approaches: one based on inserting completely new words into sentences generated from templates, and another based on replacing words with synonyms. Our initial results from a human evaluation conducted in English indicate that these approaches successfully improve the variety of the language without significantly modifying sentence meaning. We also present variations of the methods applicable to low-resource languages, simulated here using Finnish, where cross-lingual aligned embeddings are harnessed to make use of linguistic resources in a high-resource language. A human evaluation indicates that while proposed methods show potential in the low-resource case, additional work is needed to improve their performance.

Human Interactive Method for a Ground Target Tracking Using Airborne Imaging System

This research presents a human interactive method for ground target tracking using airborne imaging system. This method depends on Template Matching using Zero Mean Normalized Cross Correlation.

Developing A Comprehensive Accident Report Template For Syria And Evaluating The Limitation In The Currently

The primary objective of this research is to assess the state of the road accidents’ data currently collected by using traditional text reports in the Syrian Arab Republic. The followed approach consisted of two main steps: (1) Developing a comprehensive road accident report template which contains all data items that should collected from a road accident . (2) Digitizing data from randomly selected traditional road accident text reports into a computer database developed based on the accident report template established in step 1.

Study of the Upper Ordovician - Current Time tectonic evolution of the Elward Area in the Syrian Euphrates Graben Using restoration techniques

The inclined shear restoration technique was used in this research as the primary method to remove the effects of fault displacements. These displacements were resulted from the impact of the NE-SW trending extensional forces. The inclined shear restoration technique was applied to the NE-SW trending seismic section (Inline A2157) along the Elward Area, using 2D move software. The vertical shear restoration technique was used as a complementary method to remove the effects of folding associated with faulting, especially to formations under the Base Upper Cretaceous Unconformity (BKU). The inclined shear and the vertical shear restoration techniques contrib uted to build many geological sections according to depth seismic section (Inline A 2157). These sections showed the Tectonic setting of the study area from Upper Ordovician till current time.