Artificial-intelligence systems capable of asking, managing, and answering questions while incrementally processing and analyzing texts can enhance: story comprehension and prediction, automatic item generation, synthetic data generation, automatic text summarization, the design and engineering of instructional materials and technical documentation, and the writing of stories.

Historical approaches to computationally modeling the various interdependent and integrated components of reading have included modeling: eye movement, word identification, sentence processing, discourse representation, and overall reading architectures.

Computational models of reading architectures include: the Attention Shift Model, E-Z Reader, EMMA, SWIFT, Glenmore, SERIF, OB1-Reader, and Über-Reader (Reichle, 2021).

In 2006, the task-based relevance and content extraction model was developed (TRACE; Rouet, 2006). In 2011, a multi-document version was developed (MD-TRACE; Rouet & Britt, 2011). In 2017, a model was developed which expanded upon these, phrasing reading as problem-solving (RESOLV; Rouet, Britt, & Durik, 2017).

The RESOLV model argues that the activities of reading are more contextual than previous research indicated (Britt, Rouet, & Durik 2017). RESOLV proposes two cognitive representations: the context model and the task model.

There are interesting relationships between dynamic and unfolding informational needs and contexts during reading. Answering questions from requesters or from readers themselves can be components of reading tasks. Readers' goals and subgoals could include acquiring that information with which to satisfy their dynamic and unfolding informational needs.

Of mathematics, Georg Cantor said that the art of asking questions is more valuable than solving problems.

In artificial intelligence, the capability to generate the right questions is highly sought after to reflect the ability to understand language, to gather new information, and to engage with users (Ko, Chen, Huang, Durrett, & Li, 2020).

With respect to reading comprehension, it is plausible to assume that, as content is processed, previous questions are answered and new ones are raised (Olson, Duffy, & Mack, 2017).

While it is not posited that those reading silently are consciously asking themselves questions as texts are processed, texts' contents are understood and added to growing mental representations and existing informational needs, or questions, interact with arriving content to produce subsequent informational needs, or questions (Olson, Duffy, & Mack, 2017).

It would be impractical to maintain a list of pending questions during reading and to check each question every time that a new fact was encountered (Ram, 1991).

Questions should, then, be indexed in memory. As they are indexed, it is likely that readers would find answers to questions other than those they are primarily focused upon. Readers' informational needs or knowledge goals, then, can be satisfied opportunistically during reading (Ram, 1991).

Readers can use those questions which arise during reading to focus their inferencing (Ram, 1989). However, not all questions are equally important and not all answers are equally valuable to readers.

There are two types of heuristics for ascribing interestingness to content during reading: content-based and structure- or configuration-based (Ram, 1989). In content-based heuristics, some things are more interesting to a reader based upon the reader's goals. In structure- or configuration-based heuristics, some kinds of situations, e.g., expectation failures, are more interesting to a reader than others. In general, both of these heuristics must be combined to determine overall interestingness.

With respect to the focusing of attention, there are two ways to attribute value to factual statements during text or story processing: top-down and bottom-up (Ram, 1991). In the top-down way, facts which answer questions are worth focusing attention upon as they help to satisfy readers' informational needs or knowledge goals. In the bottom-up way, facts which raise new questions are worth focusing attention upon, in particular if they arise from gaps or inconsistencies in readers' knowledge.

Several models have been developed which describe cognitive processes involved in answering questions, whether from memory or from consulting external resources.

In 1978, Lehnert developed a computational model of question-answering, QUALM (Lehnert, 1978). It was implemented as a computer program that interoperated with two different story-comprehension systems.

In 1990, the QUEST model was developed (Graesser & Franklin, 1990). It was comprised of four procedural components: question interpretation, identification of relevant information sources, pragmatics, and convergence mechanisms.

Taxonomies of questions have encompassed: recognition, recall, comprehension, application, analysis, synthesis, and evaluation (Bloom, 1956).

Taxonomies of questions have encompassed: causal antecedent, goal orientation, enablement, causal consequent, verification, disjunctive, instrumental/procedural, concept completion, expectational, judgmental, quantification, feature specification, and request (Lehnert, 1978).

Later taxonomies have added: example, definition, comparison, and interpretation (Graesser & Person, 1994).

There are also to consider: guiding questions and inquisitive questions. Guiding questions are those questions provided by texts' authors to direct readers in their searches for understanding. Inquisitive questions arise from readers' curiosity.

Questions can also be described as being either: literal or inferential. Literal questions focus on information directly stated in the text, involving surface-level understandings. Inferential questions require readers to make inferences, involving deeper understandings and interpretations of the text.

Taxonomies of answers have encompassed: direct, indirect, partial, limiting, correcting, modifying, and accurate and exhaustive answers (Brożek, 2011).

Inferences during reading can be: automatic or strategic; online or offline; text-connecting, knowledge-based, or extratextual; local or global; coherence or elaborative; unconscious or conscious; bridging; intersentence or text-connecting, or gap-filling; coherence, elaborative, knowledge-based, or evaluative; and anaphoric, text-to-text, or background-to-text (Kispal, 2008).

Types of inferences during reading include: referential; filling in deleted information; inferring the meanings of words; inferring connotations of words or sentences; relating text to prior knowledge; inferences about the author; inferences about characters; inferences about the state of the world as depicted; confirming or disconfirming previous inferences; and drawing conclusions (Pressley & Afflerbach, 1995).

Types of inferences during story-reading include: referential; case structure role assignment; antecedent causal; superordinate goal; thematic; character emotion; causal consequence; instantiation noun category; instrument; subordinate goal action; state; reader's emotion; and author's intent (Graesser, Singer, & Trabasso, 1994).

Erotetic inferences are processes through which questions are inferred from assertions, questions, or combinations of assertions and questions.

Question decomposition, for example, transforming compound questions into simpler ones, is a type of erotetic inference.

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